o Si@sfddlZddlZddlZddlmZmZmZGdddejZGdddejZ GdddejZ dS) N)nn view_as_realview_as_complexc sJeZdZdZd dedededeffdd Zd ejd ejfd d Z Z S)ISTFTa Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges. See issue: https://github.com/pytorch/pytorch/issues/62323 Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs. The NOLA constraint is met as we trim padded samples anyway. Args: n_fft (int): Size of Fourier transform. hop_length (int): The distance between neighboring sliding window frames. win_length (int): The size of window frame and STFT filter. padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". samen_fft hop_length win_lengthpaddingcsLt|dvr td||_||_||_||_t|}| d|dS)Ncenterr#Padding must be 'center' or 'same'.window) super__init__ ValueErrorr rrr torch hann_windowregister_buffer)selfrrr r r __class__F/home/ubuntu/.local/lib/python3.10/site-packages/vocos/spectral_ops.pyrs  zISTFT.__init__specreturnc CsV|jdkrtj||j|j|j|jddS|jdkr"|j|jd}ntd|dks0Jd|j \}}}tj j ||jd d d }||jd d d d f}|d |j|j}tj j j|d |fd |jfd |jfd d d dd|| f}|jd |dd d} tj j j| d |fd |jfd |jfd || } | dksJ|| }|S)a Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram. Args: spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size, N is the number of frequency bins, and T is the number of time frames. Returns: Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal. r T)r rr zExpected a 3D tensor as inputbackward)dimnormN output_size kernel_sizestridergdy=)r ristftrrr rrr shapefftirfftr functionalfoldsquareexpand transposesqueezeall) rrpadBNTifftr#y window_sqwindow_enveloperrrforward!s0    z ISTFT.forwardr __name__ __module__ __qualname____doc__intstrrrTensorr: __classcell__rrrrrs  rcBeZdZdZd dedeffdd Zdejdejfd d Z Z S) MDCTz Modified Discrete Cosine Transform (MDCT) module. Args: frame_len (int): Length of the MDCT frame. padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". r frame_lenr cst|dvr td||_||_|d}|dd}ttj | }| d|t dtj t||}t dtj |t|d|}| dt|| d t|dS) Nr r rrryg? pre_twiddle post_twiddlerrrr rGr from_numpyscipysignalcosinefloatrexppiarangerrrGr r4n0rrHrIrrrrWs   &z MDCT.__init__audiorcCs|jdkrtjj||jd|jdf}n|jdkr,tjj||jd|jdf}ntd|d|j|jd}|jd}||j |j }tj j |t |j  |j dddd |f}|t |j |j td |}t|tdS) a Apply the Modified Discrete Cosine Transform (MDCT) to the input audio. Args: audio (Tensor): Input audio waveform of shape (B, T), where B is the batch size and T is the length of the audio. Returns: Tensor: MDCT coefficients of shape (B, L, N), where L is the number of output frames and N is the number of frequency bins. r rrr r&r .Nr)r rrr+r2rGrunfoldrr.r(r)rrHrInpsqrtreal)rrUxr4Xresrrrr:is " " .$z MDCT.forwardr;r<rrrrrFNsrFcrE) IMDCTz Inverse Modified Discrete Cosine Transform (IMDCT) module. Args: frame_len (int): Length of the MDCT frame. padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". rrGr cst|dvr td||_||_|d}|dd}ttj | }| d|t dtj |t|d|}t dtj t|d||d}| dt|| dt|dS) Nr r rrry?rHrIrJrSrrrrs   &*zIMDCT.__init__r]rc Csb|j\}}}tj|||df|j|jd}||dd|f<dttj|dd|d|df<tjj|t |j  |jdd}t |t |j  |jt|td}||j |j}d |d |f}tjjj|d d|d |jfd |jdfd ddd d ddf} |jd kr|jd} n|jd kr|jd} ntd| dd| | f} | S)a Apply the Inverse Modified Discrete Cosine Transform (IMDCT) to the input MDCT coefficients. Args: X (Tensor): Input MDCT coefficients of shape (B, L, N), where B is the batch size, L is the number of frames, and N is the number of frequency bins. Returns: Tensor: Reconstructed audio waveform of shape (B, T), where T is the length of the audio. r)dtypedevice.Nr&)r&)dimsrWrr"rr rrVr )r(rzerosr`raconjflipr)r6rrHr.r[rIrYrZrrr+r,r/rGr r) rr]r3Lr4Yr7resultr#rUr2rrrr:s. $"0      z IMDCT.forwardr;r<rrrrr_sr_) numpyrYrLrrrrModulerrFr_rrrrsG7