,j ,SrSSKrSSKrSSKrSSKJr S\R4Sjr SS\RS\ S\ S \ 4S jjr S \R4S jrS \R4SjrS\ S\ S\ 4SjrSS\R4SjjrSS\ S\ S\ 4SjjrS\ S\ 4Sjrg)z1 Signal processing or PyTorch related utilities. N) functionalxc [R"US:H[R"SURURS9[R "U5U- 5$)z\ Implementation of sinc, i.e. sin(x) / x __Warning__: the input is not multiplied by `pi`! rg?)devicedtype)torchwheretensorrrsin)rs ڙC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\julius/core.pysincr sC ;;qAvu||BqxxqwwOQVQZQZ[\Q]`aQa bbr target_lengthmodevaluecP[R"USXRS- 4X#S9$)zA Pad the given tensor to the given length, with 0s on the right. r)rr)Fpadshape)r rrrs r pad_tors( 55!]\\"-==>T WWrfreqsc@S[R"SUS- -5-$)z Converts a Tensor of frequencies in hertz to the mel scale. Uses the simple formula by O'Shaughnessy (1987). Args: freqs (torch.Tensor): frequencies to convert. # )rlog10)rs r hz_to_melrs  %++a%#+o. ..rmelscSSUS- -S- -$)z Converts a Tensor of mel scaled frequencies to Hertz. Uses the simple formula by O'Shaughnessy (1987). Args: mels (torch.Tensor): mel frequencies to convert. r rr)rs r mel_to_hzr#)s "td{#a' ((rn_melsfminfmaxc[[R"[U555R 5n[[R"[U555R 5n[R "X4U5n[ U5$)z Return frequencies that are evenly spaced in mel scale. Args: n_mels (int): number of frequencies to return. fmin (float): start from this frequency (in Hz). fmax (float): finish at this frequency (in Hz). )rrr floatitemlinspacer#)r$r%r&lowhighrs r mel_frequenciesr-4s^ ELLt- . 3 3 5C U\\%+. / 4 4 6D >>#V ,D T?rc\[R"XS-RS5-5S-$)z Return the volume in dBFS. rr!)rrmean)rfloors r volumer2Es' ;;u1{{2. /" 44rfreqsrdurc[R"[X-5US9R5U- n[R"S[ R -U-U-5$)z Return a pure tone, i.e. cosine. Args: freq (float): frequency (in Hz) sr (float): sample rate (in Hz) dur (float): duration (in seconds) )rr/)rarangeintr(cosmathpi)r3r4r5rtimes r pure_toner=LsI <<BH f 5 ; ; = BD 99Q[4'$. //r kernel_sizestridec[UR5nURS5n[R"[ XA5U- U- 5S-nUS- U-U-n[ R"USXd- 45R5n/n[UR55H#n URURU 55 M% URS5n U S:XdS5eXS/-nURX5U/-U5$)a1D only unfolding similar to the one from PyTorch. However PyTorch unfold is extremely slow. Given an input tensor of size `[*, T]` this will return a tensor `[*, F, K]` with `K` the kernel size, and `F` the number of frames. The i-th frame is a view onto `i * stride: i * stride + kernel_size`. This will automatically pad the input to cover at least once all entries in `input`. Args: input (Tensor): tensor for which to return the frames. kernel_size (int): size of each frame. stride (int): stride between each frame. Shape: - Inputs: `input` is `[*, T]` - Output: `[*, F, kernel_size]` with `F = 1 + ceil((T - kernel_size) / stride)` ..Warning:: unlike PyTorch unfold, this will pad the input so that any position in `input` is covered by at least one frame. rrrzdata should be contiguous)listrpopr:ceilmaxrr contiguousrangedimappendr? as_strided) inputr>r?rlengthn_frames tgt_lengthpaddedstridesrG last_strides r unfoldrQYs.  E YYr]Fyy#f2[@FJKaOHQ,&(;6J UU51j12 3 > > @FGVZZ\"v}}S)*#++b/K ! 888  #G   U %<)N)__doc__r:typingtprtorch.nnrrTensorr r8strr(rrr#r-r2r=rQr"rr r[s  $cELLcX5<<XX3XTYX /U\\ /)ELL)CuE"5ell5 0E 0u 0 0"Gs"GC"Gr