o 2wi @sdZddlZddlZddlZddlmZdejfddZ d'dejd e d e d e fd d Z dejfddZdejfddZde de de fddZd(dejfddZd)de de d e fd!d"Zd#e d$e fd%d&ZdS)*z1 Signal processing or PyTorch related utilities. N) functionalxcCs,t|dktjd|j|jdt||S)zh Implementation of sinc, i.e. sin(x) / x __Warning__: the input is not multiplied by `pi`! rg?)devicedtype)torchwheretensorrrsin)rr H/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/julius/core.pysinc s,r constantr target_lengthmodevaluecCs tj|d||jdf||dS)zI Pad the given tensor to the given length, with 0s on the right. r)rr)Fpadshape)rrrrr r r pad_tos rfreqscCsdtd|dS)z Converts a Tensor of frequencies in hertz to the mel scale. Uses the simple formula by O'Shaughnessy (1987). Args: freqs (torch.Tensor): frequencies to convert. # )rlog10)rr r r hz_to_mels rmelscCsdd|ddS)z Converts a Tensor of mel scaled frequencies to Hertz. Uses the simple formula by O'Shaughnessy (1987). Args: mels (torch.Tensor): mel frequencies to convert. r rrr )rr r r mel_to_hz)srn_melsfminfmaxcCsBttt|}ttt|}t|||}t|S)z Return frequencies that are evenly spaced in mel scale. Args: n_mels (int): number of frequencies to return. fmin (float): start from this frequency (in Hz). fmax (float): finish at this frequency (in Hz). )rrrfloatitemlinspacer)rr r!lowhighrr r r mel_frequencies4s r':0yE>cCst||dddS)z$ Return the volume in dBFS. rr)rrmean)rfloorr r r volumeEsr,freqsrdurcCs6tjt|||d|}tdtj||S)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)r/r0r1rtimer r r pure_toneLs r8 kernel_sizestridec Cst|j}|d}tt||||d}|d||}t|d||f}g}t | D] } | | | q4|ddksJJd||dg}| |||g|S)aV1D 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)listrpopr5ceilmaxrr contiguousrangedimappendr: as_strided) inputr9r:rlengthn_frames tgt_lengthpaddedstridesrAr r r unfoldYs   rJ)r r)r()r-r.N)__doc__r5typingtprtorch.nnrrTensorr r3strr"rrrr'r,r8rJr r r r s