~Vji/UddlZddlmZmZmZddlZddlmZddlmcm Z de de fdZ de dfZ ee e fed<Gd d ejZGd d ejZGd dejZGddejZGddejZGddejZdedededejjfdZ d"dededededededededeedefd Zdefd!ZdS)#N)ListOptionalTuplexreturncHdddtjd|zdzzz zS)aThe metric defined by ITU-T P.862 is often called 'PESQ score', which is defined for narrow-band signals and has a value range of [-0.5, 4.5] exactly. Here, we use the metric defined by ITU-T P.862.2, commonly known as 'wide-band PESQ' and will be referred to as "PESQ score". Args: x (float): Narrow-band PESQ score. Returns: (float): Wide-band PESQ score. g+?g@g;pΈgׁsF@)mathexp)rs [/root/voice-cloning/.venv/lib/python3.11/site-packages/torchaudio/models/squim/objective.pytransform_wb_pesq_ranger s, Ma$(7Q;3G*H*H&HI II?g@ PESQRangec^eZdZddeeefddffd ZdejdejfdZxZ S) RangeSigmoidr val_rangerNctt|t|trt |dksJ||_tj|_ dS)N) superr__init__ isinstancetuplelenrnnSigmoidsigmoid)selfr __class__s r rzRangeSigmoid.__init__ s] lD!!**,,,)U++CI!0C0C0CC.7*,*,, rrc|||jd|jdz z|jdz}|S)Nr r)rrr routs r forwardzRangeSigmoid.forward&s;ll1oo!2T^A5F!FG$.YZJ[[ r)r) __name__ __module__ __qualname__rfloatrtorchTensorr% __classcell__r!s@r rrs77%u "57t777777 %,rrcVeZdZdZd dededdffd Zd ejdejfd ZxZ S) EncoderzEncoder module that transform 1D waveform to 2D representations. Args: feat_dim (int, optional): The feature dimension after Encoder module. (Default: 512) win_len (int, optional): kernel size in the Conv1D layer. (Default: 32)  feat_dimwin_lenrNctt|tjd|||dzd|_dS)Nr rF)stridebias)rr/rrConv1dconv1d)r r2r3r!s r rzEncoder.__init__3sD gt%%'''i8WW\PUVVV rrc|d}tj||}|S)a Apply waveforms to convolutional layer and ReLU layer. Args: x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`. Returns: (torch,Tensor): Feature Tensor with dimensions `(batch, channel, frame)`. r dim) unsqueezeFrelur8r#s r r%zEncoder.forward8s7kkak  fT[[%%&& r)r0r1) r&r'r(__doc__intrr*r+r%r,r-s@r r/r/+sWWWSW$WWWWWW  %,        rr/c ZeZdZd dededededdf fd Zd ejdejfd Z xZ S) SingleRNNrrnn_type input_size hidden_sizedropoutrNc tt|||_||_||_t t|||d|dd|_tj |dz||_ dS)Nr T)rF batch_first bidirectionalr) rrBrrCrDrEgetattrrrnnLinearproj)r rCrDrErFr!s r rzSingleRNN.__init__Gs i'')))  $&&;gb(&;&;    ' ' ' IkAoz:: rrc`||\}}||}|SN)rKrM)r rr$_s r r%zSingleRNN.forwardYs)!Qiinn r)r) r&r'r(strr@r)rr*r+r%r,r-s@r rBrBFs;;;#;C;RW;bf;;;;;;$%,rrBceZdZdZ dd ed ed ed ed edededdffd Zdejde ejeffdZ dejde ejeffdZ dejdedejfdZ dejdejfdZ xZS)DPRNNa*Dual-path recurrent neural networks (DPRNN)* :cite:`luo2020dual`. Args: feat_dim (int, optional): The feature dimension after Encoder module. (Default: 64) hidden_dim (int, optional): Hidden dimension in the RNN layer of DPRNN. (Default: 128) num_blocks (int, optional): Number of DPRNN layers. (Default: 6) rnn_type (str, optional): Type of RNN in DPRNN. Valid options are ["RNN", "LSTM", "GRU"]. (Default: "LSTM") d_model (int, optional): The number of expected features in the input. (Default: 256) chunk_size (int, optional): Chunk size of input for DPRNN. (Default: 100) chunk_stride (int, optional): Stride of chunk input for DPRNN. (Default: 50) @LSTMd2r2 hidden_dim num_blocksrCd_model chunk_size chunk_striderNcJtt|||_t jg|_t jg|_t jg|_t jg|_ t|D]}|j t||||j t||||j t j d|d|j t j d|dt jt j||dt j|_||_||_dS)Nr g:0yE>)eps)rrSrr\r ModuleListrow_rnncol_rnnrow_normcol_normrangeappendrB GroupNorm SequentialConv2dPReLUconvr^r_) r r2r[r\rCr]r^r_rPr!s r rzDPRNN.__init__ms^ eT##%%%$}R(( }R((  b))  b)) z"" F FA L   (Hj I I J J J L   (Hj I I J J J M at!D!D!D E E E M at!D!D!D E E E EM Ih + + HJJ   %(rrc|jd}|j|j||jzz|jzz }tj||j||jzg}||fS)N)shaper^r_r=pad)r rseq_lenrestr$s r pad_chunkzDPRNN.pad_chunks\'"+$"3g6O"OSWSb!bbeA)4$2C+CDEEDyrc||\}}|j\}}}|ddddd|j f||d|j}|dddd|jdf||d|j}t j||gd}|||d|jdd}||fS)Nror:r) rtrpr_ contiguousviewr^r*cat transpose) r rr$rs batch_sizer2rr segments1 segments2s r chunkingzDPRNN.chunkingsNN1%% T(+ % Hg1112!2 2223>>@@EEjRZ\^`d`opp 111d/1112==??DDZQY[]_c_noo iI.A666hhz8RAAKKAqQQ\\^^Dyrrsc^|j\}}}}|dd||d|jdz}|ddddddd|jf||ddddd|jdf}|dddddd|jdf||dddddd|j f}||z}|dkr|ddddd| f}|}|S)Nrrvror)rprzrwrxr^r_) r rrsr{r;rPr$out1out2s r mergingz DPRNN.mergingsh ! CAkk!Q**,,11*c2tYZGZ[[111aaa-do--.99;;@@SRTUUVWVWVWYZYZYZ\`\m\o\oVop111aaaDO---.99;;@@SRTUUVWVWVWYZYZYZ\p_c_p^p\pVpqTk !88aaaFdUFl#Cnn rc||\}}|j\}}}}|}t|j|j|j|jD]f\}} } } |dddd ||z|d} || } | |||ddddd} | | } || z}|dddd ||z|d}| |}| |||ddddd}| |}||z}h| |}| ||}| dd}|S)Nrrvrr ro) r~rpziprcrerdrfpermuterwrxrmrrz)r rrsr{rPdim1dim2r$rcrerdrfrow_inrow_outcol_incol_outs r r%z DPRNN.forwards--""4$%G! AtT47 dmUYUacgcp4q4q 0GXw[[Aq!,,7799>>zD?PRVXZ[[ffhhFgfooGll:tT2>>FFq!QPQRR]]__Ghw''G-C[[Aq!,,7799>>zD?PRVXZ[[ffhhFgfooGll:tT2>>FFq!QPQRR]]__Ghw''G-CCiinnll3%%mmAq!!,,.. r)rTrUrVrWrXrYrZ)r&r'r(r?r@rQrr*r+rrtr~rr%r,r-s@r rSrS`sd  )))) )  )  ))) ))))))<5<E%,2C,D %, 5s1B+C      S U\    %,rrScNeZdZddeddffd ZdejdejfdZxZS) AutoPoolr pool_dimrNc tt|||_t j||_|dt jtj ddS)Nr:alphar ) rrrrrSoftmaxsoftmaxregister_parameter Parameterr*ones)r rr!s r rzAutoPool.__init__sh h&&(((% *,**B*B*B  ejmm)D)DEEEEErrc|tj||j}tjtj|||j}|S)Nr:)rr*mulrsumr)r rweightr$s r r%zAutoPool.forwardsHei4:6677i !V,,$-@@@ r)r ) r&r'r(r@rr*r+r%r,r-s@r rrsFFFTFFFFFF %,rrc~eZdZdZdejdejdejffd Zdej de ej fdZ xZ S) SquimObjectiveaSpeech Quality and Intelligibility Measures (SQUIM) model that predicts **objective** metric scores for speech enhancement (e.g., STOI, PESQ, and SI-SDR). Args: encoder (torch.nn.Module): Encoder module to transform 1D waveform to 2D feature representation. dprnn (torch.nn.Module): DPRNN module to model sequential feature. branches (torch.nn.ModuleList): Transformer branches in which each branch estimate one objective metirc score. encoderdprnnbranchesctt|||_||_||_dSrO)rrrrrr)r rrrr!s r rzSquimObjective.__init__s: nd##,,...    rrrcb|jdkrtd|jd|tj|dzdddzdzz }||}||}g}|jD]4}|||d 5|S) z Args: x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`. Returns: List(torch.Tensor): List of score Tenosrs. Each Tensor is with dimension `(batch,)`. rz/The input must be a 2D Tensor. Found dimension .r T)r;keepdimg?r:) ndim ValueErrorr*meanrrrrhsqueeze)r rr$scoresbranchs r r%zSquimObjective.forwards 6Q;;XqvXXXYY Y AqDa666#=B Cll1oojjoom 6 6F MM&&++--!-44 5 5 5 5 r) r&r'r(r?rModulerbrr*r+rr%r,r-s@r rrs ! !y !- ! ! ! ! ! !$u|*<rrr]nheadmetricctj|||dzdd}t}|dkr[tjtj||tjtj|dt }n|dkrbtjtj||tjtj|dt t}nMtjtj||tjtj|d}tj|||S) alCreate branch module after DPRNN model for predicting metric score. Args: d_model (int): The number of expected features in the input. nhead (int): Number of heads in the multi-head attention model. metric (str): The metric name to predict. Returns: (nn.Module): Returned module to predict corresponding metric score. rT)rFrHstoir pesq)r)rTransformerEncoderLayerrrjrLrlrr)r]rrlayer1layer2layer3s r _create_branchrs '! S^b c c cF ZZF  Igw ' ' HJJ Igq ! ! NN    6   Igw ' ' HJJ Igq ! ! 9 - - -   %'M")GW2M2Mrxzz[][delno[p[p$q$q = 0 00rr2r3r[r\rCr^r_c ||dz}t||} t|||||||} tjt ||dt ||dt ||dg} t | | | S)aBuild a custome :class:`torchaudio.models.squim.SquimObjective` model. Args: feat_dim (int, optional): The feature dimension after Encoder module. win_len (int): Kernel size in the Encoder module. d_model (int): The number of expected features in the input. nhead (int): Number of heads in the multi-head attention model. hidden_dim (int): Hidden dimension in the RNN layer of DPRNN. num_blocks (int): Number of DPRNN layers. rnn_type (str): Type of RNN in DPRNN. Valid options are ["RNN", "LSTM", "GRU"]. chunk_size (int): Chunk size of input for DPRNN. chunk_stride (int or None, optional): Stride of chunk input for DPRNN. Nrrrsisdr)r/rSrrbrr) r2r3r]rr[r\rCr^r_rrrs r squim_objective_modelrs0!Q h((G (J HgzS_ ` `E} 7E6 2 2 7E6 2 2 7E7 3 3 H '5( 3 33rc 0tddddddddS)zSBuild :class:`torchaudio.models.squim.SquimObjective` model with default arguments.rXrTrrrWG)r2r3r]rr[r\rCr^)rrr squim_objective_baser;s1     rrO)r typingrrrr*torch.nnrtorch.nn.functional functionalr=r)r r__annotations__rrr/rBrSrrr@rQmodulesrrrrrr rs ((((((((((  Ju J J J J JC  " 5      29   bi6 4]]]]]BI]]]@     ry   %%%%%RY%%%P1C11S1RZ=N1111R#'#4#4#4 #4#4  #4  #4  #4#4#43-#4#4#4#4#4L n      r