+ j~Rt^RIt^RIHt^RIHt^RIHtH t H t ] P!] 4t !RR]4tR.tR#)zFeature extractor class for DACN)SequenceFeatureExtractor) BatchFeature)PaddingStrategy TensorTypeloggingcdaa]tRt^toRtRR.tR V3RlV3RllltR V3RlRlltRtVt V;t #) DacFeatureExtractora  Constructs an Dac feature extractor. This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: feature_size (`int`, *optional*, defaults to 1): The feature dimension of the extracted features. Use 1 for mono, 2 for stereo. sampling_rate (`int`, *optional*, defaults to 16000): The sampling rate at which the audio waveform should be digitalized, expressed in hertz (Hz). padding_value (`float`, *optional*, defaults to 0.0): The value that is used for padding. hop_length (`int`, *optional*, defaults to 512): Overlap length between successive windows. input_values n_quantizersc2<V^8dQhRS[RS[RS[RS[/#) feature_size sampling_rate padding_value hop_length)intfloat)format __classdict__s"o/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/dac/feature_extraction_dac.py __annotate__ DacFeatureExtractor.__annotate__.s3 % % % % %  %c @<\SV`!RRVRVRV/VBW@nR#)r rrN)super__init__r)selfr rrrkwargs __class__s&&&&&,rrDacFeatureExtractor.__init__.s+ wlw-w_lwpvw$rcR<V^8dQhRS[PS[S[,,S[S[P,,S[S[S[,,,RS[S[,S[,R,RS[R,RS[R,RS[S[,R,RS[R,RS[ /#) r raw_audiopaddingN truncation max_lengthreturn_tensorsrreturn) npndarraylistrboolstrrrrr)rrs"rrr9snn::U +d2::.>>d5kARRno-4n4K n $J n j(4/ nTzn nrc  2Ve>W`P8wd-\RV RVP RVP RV R2 4hM-\PRVPP R24V'dV'd \R4hVfR p\ \V\\34;'d-\V^,\P\\344pV'dAVUu.uF3p\P!V\PR 7PNK5 ppMV'gH\V\P4'g(\P!V\PR 7pMq\V\P4'dRVP\P!\P 4Jd VP#\P4pV'g"\P!V4P.p\%V4FwrV P&^8d\R V P( 24hVP*^8Xd2V P&^8wd!\R V P(R, R 24hVP*^8XgK\R4h \-RV/4p VP/V VVVVVP0R7p V'dV P3R4V R&V'd*V P4R\P6R3,V n.p V P3R4F8p VP*^8Xd V R,p V P9V P4K: WR&VeV P;V4p V #uupi)a Main method to featurize and prepare for the model one or several sequence(s). Args: raw_audio (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`): The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. The numpy array must be of shape `(num_samples,)` for mono audio (`feature_size = 1`), or `(2, num_samples)` for stereo audio (`feature_size = 2`). padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, *optional*, defaults to `False`): Activates truncation to cut input sequences longer than `max_length` to `max_length`. max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). return_tensors (`str` or [`~utils.TensorType`], *optional*, default to 'pt'): If set, will return tensors instead of list of python integers. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. sampling_rate (`int`, *optional*): The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors. z3The model corresponding to this feature extractor: z& was trained using a sampling rate of zB. Please make sure that the provided audio input was sampled with z and not .zDIt is strongly recommended to pass the `sampling_rate` argument to `zN()`. Failing to do so can result in silent errors that might be hard to debug.zABoth padding and truncation were set. Make sure you only set one.T)dtypez6Expected input shape (channels, length) but got shape z$Expected mono audio but example has z channelsz$Stereo audio isn't supported for nowr )r%r$r#return_attention_maskpad_to_multiple_ofattention_mask padding_mask:NNN).N)r ValueErrorloggerwarningr__name__r+ isinstancer*tupler(r)asarrayfloat32Tr/float64astype enumeratendimshaper rpadrpopr newaxisappendconvert_to_tensors) rr"r#r$r%r&r is_batchedaudioidxexampler padded_inputss &&&&&&& r__call__DacFeatureExtractor.__call__9sR  $ 2 22 I$P**+,**+9]O1F3 NNVW[WeWeWnWnVop\\  z`a a _G y4- 0 j jj1PRPZPZ\acgOh6i  LUVI5E<>>IIVIJy"**$E$E 9BJJ?I  2:: . .9??bhhrzzFZ3Z!((4II.001I&i0LC||a #YZaZgZgYh!ijj  A%',,!*; #G VXHYGZZc!dee  A% !GHH 1$^Y$?@  !!")# !  ,9,=,=>N,OM. ) )6)C)CArzzSTDT)UM & $((8G  A%!),    *9 )5n%  %)<<^LM[Ws/9N)r)i>gi)NFNNN) r8 __module__ __qualname____firstlineno____doc__model_input_namesrrM__static_attributes____classdictcell__ __classcell__)rrs@@rrrs3"(8 % %nnnrr)rSnumpyr(!feature_extraction_sequence_utilsrfeature_extraction_utilsrutilsrrr get_loggerr8r6r__all__rrrr^sE&I499   H %M2M` ! !r