yjgcrddlmZddlZddlZddlmZddlZddlZerddlm Z ddl m Z ddl m Z ddlmZmZmZmZGd d ZeZedTdZedUdVdZe dUdWdZedUdVdZedXdZedYd ZedZd!Zed[d$Ze dUdVd%Ze dUd\d(Ze dUd]d*Z e d^dVd-Z!edUdVd.Z"edUdVd/Z#e d_dVd1Z$e dUd`d3Z%e dadVd5Z&edUdVd6Z'edUdVd7Z(edUdVd8Z)edUdVd9Z* dUdbd>Z+ddd?d?d@dcdGZ, dddJddd?d?dKdedNZ- dfdJddd?d?dKdgdOZ. dhdJddd?d?dKdidQZ/ dfdJddd?d?dKdgdRZ0 dfdJddd?d?dKdgdSZ1dS)j) annotationsN) TYPE_CHECKING)Tensor) PlaceLike)_WindowLiteral)_current_expected_place_get_paddle_placecorein_dynamic_or_pir_modec"eZdZdZddZdZdS)WindowFunctionRegisterci|_dSN_functions_dict)selfs n/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/audio/functional/window.py__init__zWindowFunctionRegister.__init__&s!Ncfd}|S)Nc*|j}|j|<|Sr)__name__r)funcnamers radd_subfunctionz8WindowFunctionRegister.register..add_subfunction*s=D)-D  &Kr)rrrs` rregisterzWindowFunctionRegister.register)s$      rc|j|Srr)rrs rgetzWindowFunctionRegister.get1s#D))rr)r __module__ __qualname__rrr rrrrr%sF"""*****rrx list[Tensor] data_typestrreturnrc.g}|D]}}tj|r?t|ts*|t j|g|U|t j||~t j|Sr)npisscalar isinstancer&appendpaddle to_tensorconcat)r#r%lts r_catr28s A 55 ;q>> 5*Q"4"4 5 HHV%qc955 6 6 6 6 HHV%a33 4 4 4 4 =  rTfloat64Mintsymbooldtypecvt|rtj|f|St||\}}tjd||}tj||}tjtj||dz dz d|z|dz z dd|z|dz z z }t||S)zm Computes the Bartlett window. This function is consistent with scipy.signal.windows.bartlett(). r8r@) _len_guardsr-ones_extendaranger.where less_equal _truncater4r6r8 needs_truncnws r _bartlettrHCs 1~~.{A4u----Q__NA{ a%(((A%(((A !a!es]++ a1q5 cAgQ  A Q $ $$rbetafloatct|rtj|f|St||\}}tj||}tjd||}tj||}|dz dz }tj|tjd||z |z dzz ztj|z }t||S)zcCompute the Kaiser window. This function is consistent with scipy.signal.windows.kaiser(). r:rr;r<) r=r-r>r?r.r@i0sqrtrC)r4rIr6r8rErFalpharGs r_kaiserrOXs1~~.{A4u----Q__NA{  D . . .D a%(((A%(((A UcME  v{1Ue 3;;<<<   $ A Q $ $$rcXtjgd|}t||||S)zYNuttall window. This function is consistent with scipy.signal.windows.nuttall(). )gzD?g;%N?g1|?gC˅?r:)ar6r8)r-r._general_cosine)r4r6r8rQs r_nuttallrSos? 444E   A 1s% 8 8 88rTensor | floatct|tr/tj|tj|dzdz zSt j|t jt j|dz zS)Nrr;)r+rJmathlogrMr-square)r#s r_acoshrYzsi!U1xDIadQh///000 :a&+fmA&6&6&:;;; < <r?r@expabsrC)r4psigr6r8rErFrGs r_general_gaussianrfs1~~.{A4u----Q__NA{ a%(((AGs?:A 4&*QW--!a%8899A Q $ $$rrQ list[float]ct|rtj|f|St||\}}tjt j t j||}tj|f|}tt|D]%}|||tj ||zzz }&t||S)zCompute a generic weighted sum of cosine terms window. This function is consistent with scipy.signal.windows.general_cosine(). r:) r=r-r>r?linspacerVpizerosrangelencosrC)r4rQr6r8rEfacrGks rrRrRs1~~.{A4u----Q__NA{ /47(DGQe < < .Wsk1v} LLOO Jq47{R\\!__4AG c8IJQN O O     rra)r=r-r>r?rYrVrjr@empty empty_likerlrmprodsqueezerC)r4nbarsllnormr6r8rEBAs2signsm2minumberdenomrrGscaler}r~s` @@r_taylorrs1~~.{A4u----Q__NA{ sRxAq DGA qAqDD3J1,, -B q$e , , ,B taxk / / /B  b ! !EE##A#JE!$Q$K bBCGGnn  rV[ 2 q!trCxAo56 6    77 A2BFHH(=$=>>>EE 3r77Q;   A2CRC(8$8999EE+a"R&2crc7"22334+a"R&2b1fhh<"77889  %2       &-1E * * *++A aaQ! nn$ U  A Q $ $$rc(t|d||S)zCompute a Hamming window. The Hamming window is a taper formed by using a raised cosine with non-zero endpoints, optimized to minimize the nearest side lobe. HzG?r:rsr4r6r8s r_hammingrs AtS 6 6 66rc(t|d||S)zCompute a Hann window. The Hann window is a taper formed by using a raised cosine or sine-squared with ends that touch zero. ryr:rrs r_hannr s AsCu 5 5 55rryc t|rtj|f|S|dkrtj|f|S|dkrt||St ||\}}tjd||}t ||dz zdz }|d|dz}||dz||z dz }|||z dz d} ddtjtj d d|z|z |dz z zzzz} tj|j |} ddtjtj d |z dzd| z|z |dz z zzzz} tj | | | g} t| |S) z[Compute a Tukey window. The Tukey window is also known as a tapered cosine window. r:rra)r6r;r<Nryr_g) r=r-r>rr?r@r5rnrVrjshaper/rC)r4rNr6r8rErFwidthn1n2n3w1w2w3rGs r_tukeyrs1~~.{A4u---- zz{A4u---- #QC    Q__NA{ a%(((A Q#% & &E 1uqy= B 519q5y1} $ %B 1u9q=?? B FJtw"sRx%/?1q5/I*IJKKK LB RXU + + +B  *TWu q 038e3Cq1u3M MN O O P B  r2rl##A Q $ $$rstdct|rtj|f|St||\}}tjd|||dz dz z }d|z|z}tj|dz |z }t ||S)ztCompute a Gaussian window. The Gaussian widows has a Gaussian shape defined by the standard deviation(std). r:rrar<r)r=r-r>r?r@rbrC)r4rr6r8rErFsig2rGs r _gaussianr5s1~~.{A4u----Q__NA{ a%(((AGs?:A s7S=D QT7T>""A Q $ $$rracV|r|tdt|rtj|f|St ||\}}||dz dz }tjd||}tjtj||z  |z }t||S)z+Compute an exponential (or Poisson) window.Nz"If sym==True, center must be None.r:r;rr) r\r=r-r>r?r@rbrcrC)r4centertaur6r8rErFrGs r _exponentialrGs  ?v!=>>>1~~.{A4u----Q__NA{ ~a%1 a%(((A FJq6z***S011A Q $ $$rct|rtj|f|St||\}}tjd|dzdzdz|}|dzdkr+d|zdz |z }tj||dddg}n*d|z|dzz }tj||dddg}t ||S) zCompute a triangular window.r:r;rrraNr_)r=r-r>r?r@r/rCrDs r_triangr[s1~~.{A4u----Q__NA{ a!a%A)777A1uzz US[A  M1a"g, ' ' EQW  M1aBi. ) ) Q $ $$rct|rtj|f|St||\}}tjtjdd||dd}d|z tjtj|zzdtjz tj tj|zzz}td|dg|}t||S)z^Compute a Bohman window. The Bohman window is the autocorrelation of a cosine window. r:r_r;rar) r=r-r>r?rcrirnrVrjsinr2rC)r4r6r8rErorGs r_bohmanrms 1~~.{A4u----Q__NA{ *V_RAU;;;AbDA B BC SFJtw}---dg   # AA1 A aAYA Q $ $$rc,t|gd||S)aCompute a Blackman window. The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window. )gzG?ryg{Gz?r:rrrs r _blackmanrs" 1000#U C C CCrc t|rtj|f|St||\}}tjt j|z tjd||dzz}t||S)z,Compute a window with a simple cosine shape.r:rry) r=r-r>r?rrVrjr@rC)r4r6r8rErGs r_cosinersz1~~.{A4u----Q__NA{ 47Q;&-1E"B"B"BS"HIJJA Q $ $$rwindow-_WindowLiteral | tuple[_WindowLiteral, float] win_lengthfftbinsc| }d}t|tr&|d}t|dkr |dd}nPt|tr|dvrt d|zdz|}nt t |d t d |z}n"#t$r}t d |d}~wwxYw|g|R} d |i} || d |i| S) a Return a window of a given length and type. Args: window (Union[str, Tuple[str, float]]): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'gaussian', 'general_gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor', 'bartlett', 'kaiser', 'nuttall'. win_length (int): Number of samples. fftbins (bool, optional): If True, create a "periodic" window. Otherwise, create a "symmetric" window, for use in filter design. Defaults to True. dtype (str, optional): The data type of the return window. Defaults to 'float64'. Returns: Tensor: The window represented as a tensor. Examples: .. code-block:: python >>> import paddle >>> n_fft = 512 >>> cosine_window = paddle.audio.functional.get_window('cosine', n_fft) >>> std = 7 >>> gaussian_window = paddle.audio.functional.get_window(('gaussian', std), n_fft) rrr;N)gaussian exponentialkaiserzThe 'z6' window needs one or more parameters -- pass a tuple.z! as window type is not supported._zUnknown window type.r6r8) r+tuplermr&r\typewindow_function_registerr KeyError) rrrr8r6argswinstrwinfunceparamskwargss r get_windowrs38+C D&%  M v;;??!"":D FC M : : :& $33  FFDLLKKKLLL8*..sV|<< 888/00a78 4 FS\F 7F 2% 26 2 22sB00 C:C  CFlayoutdevice pin_memory requires_gradr str | NonerPlaceLike | Nonerrc2|tjdtr'|t|n t }|rt jr|t|tj tj fst|tj s.t|tj r(| rtj }nnt|tjs.t|tj r(|rtj }nt!d|||}|r't jr|}|durd|_|S)Nz1layout only supports 'strided' in Paddle; ignoredz$Pinning memory is not supported for )rTF)warningswarnr r r r-in_dynamic_moder+r CUDAPinnedPlaceXPUPinnedPlace CUDAPlacePlace is_gpu_placeXPUPlace is_xpu_place RuntimeErrortor stop_gradient)rGrrrrs r_apply_window_postprocessrs IJJJ! f % % %(**   &(( "-t/BC# &$.11 64:.. 393F3F3H3H -//FDM22 64:.. 393F3F3H3H ,.."C6CC FAf,.. LLNN Hrrq= ףp?float32)r8rrrr window_lengthperiodicctd|||} d\} } || z } || | zz } | | | zz}t|||||S)u! Compute a generalized Hamming window. Args: window_length (int): The size of the returned window. Must be positive. periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True. alpha (float, optional): The coefficient α in the equation above. Defaults to 0.54. beta (float, optional): The coefficient β in the equation above. Defaults to 0.46. dtype (str, optional): The data type of the returned tensor. Defaults to 'float32'. layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None. device(PlaceLike|None, optional): The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None. pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False. Returns: Tensor: A 1-D tensor of shape `(window_length,)` containing the Hamming window. Examples: .. code-block:: python >>> import paddle >>> win = paddle.hamming_window(400, requires_grad=True) >>> win = paddle.hamming_window(256, alpha=0.5, beta=0.5) hammingrr8)rrrrr)rrrNrIr8rrrrw0alpha0beta0rrrGs rhamming_windowrsnN I}he L L LBMFE u A F A AF A $ #    rcPtd|||}t|||||S)a; Compute a Hann window. Args: window_length (int): The size of the returned window. Must be positive. periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True. dtype (str, optional): The data type of the returned tensor. Defaults to 'float32'. layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None. device(PlaceLike|None, optional): The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None. pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False. Returns: Tensor: A 1-D tensor of shape `(window_length,)` containing the Hann window. Examples: .. code-block:: python >>> import paddle >>> win = paddle.hann_window(512) >>> win = paddle.hann_window(512, requires_grad=True) hannrrrrrr8rrrrrGs r hann_windowr-sAF 6=(%HHHA $ #    r(@cTtd|f|||}t|||||S)a Compute a Kaiser window. Args: window_length (int): The size of the returned window. Must be positive. periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True. beta (float, optional): Shape parameter for the window. Defaults to 12.0. dtype (str, optional): The data type of the returned tensor. Defaults to 'float32'. layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None. device(PlaceLike|None, optional): The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None. pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False. Returns: Tensor: A 1-D tensor of shape `(window_length,)` containing the Kaiser window. Examples: .. code-block:: python >>> import paddle >>> win = paddle.kaiser_window(400, beta=8.6) >>> win = paddle.kaiser_window(400, requires_grad=True) rrrr) rrrIr8rrrrrGs r kaiser_windowrZsOJ  4-   A % #    rcPtd|||}t|||||S)aK Compute a Blackman window. Args: window_length (int): The size of the returned window. Must be positive. periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True. dtype (str, optional): The data type of the returned tensor. Defaults to 'float32'. layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None. device(PlaceLike|None, optional): The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None. pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False. Returns: Tensor: A 1-D tensor of shape `(window_length,)` containing the Blackman window. Examples: .. code-block:: python >>> import paddle >>> win = paddle.blackman_window(256) >>> win = paddle.blackman_window(256, requires_grad=True) blackmanrrrrs rblackman_windowrsAF :}heLLLA $ #    rcPtd|||}t|||||S)al Compute a Bartlett window. Args: window_length (int): The size of the returned window. Must be positive. periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True. dtype (str, optional): The data type of the returned tensor. Defaults to 'float32'. layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None. device(PlaceLike|None, optional): The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None. pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False. Returns: Tensor: A 1-D tensor of shape `(window_length,)` containing the Bartlett window. Examples: .. code-block:: python >>> import paddle >>> n_fft = 512 >>> win = paddle.bartlett_window(n_fft) >>> win = paddle.bartlett_window(n_fft, requires_grad=True) bartlettrrrrs rbartlett_windowrsAJ :}heLLLA $ #    r)r#r$r%r&r'r)Tr3)r4r5r6r7r8r&r'r) r4r5rIrJr6r7r8r&r'r)r#rTr'r)r4r5r6r7r'r7)r4r5r'r7)rGrr]r7r'r) r4r5rQrgr6r7r8r&r'r) r4r5rNrJr6r7r8r&r'r)rtruTTr3)ryTr3) r4r5rrJr6r7r8r&r'r)NraTr3) rrrr5rr7r8r&r'r) rGrrrrrrr7rr7r'r)Trr)rr5rr7rNrJrIrJr8r&rrrrrr7rr7)T)rr5rr7r8r&rrrrrr7rr7)Tr)rr5rr7rIrJr8r&rrrrrr7rr7)2 __future__rrVrtypingrnumpyr)r-rpaddle._typingrfeatures.layersrpaddle.base.frameworkr r r r rrrr2rHrOrSrYr?r=rCrfrRrsrrrrrrrrrrrrrrrrrrrrrs#"""""   1((((((000000 * * * * * * * * 2133""$$%$""$$%%%%%$%(""$$8A%%%%%$%,""$$9999%$9""$$===%$= ""$$%$""$$%$""$$%$""$$3< % % % %%$ % ""$$;D % % % %%$ % ""$$9BFFFF%$F""$$FO5%5%5%5%%$5%p""$$7777%$7""$$6666%$6""$$6?%%%%%$%B""$$7@%%%%%$%"""$$AJ%%%%%$%&""$$%%%%%$%"""$$%%%%%$%"""$$DDDD%$D""$$%%%%%$% 3333333333r# * * * * * * ^ 2 #222222n*#******^. #......f*#******^,#,,,,,,,,r