+ j-^RIt^RIHt^RIHtHt^RIHt^RIH t ^RI H t H t H t Ht^RIHtHt]!4'd^RIt] !4'd^RIt^RIHuHt]P0!]4t]!R R7!R R ]44tR .tR#) N)Any) AudioInputmake_list_of_audio)SequenceFeatureExtractor) BatchFeature)PaddingStrategy TensorTypeis_torch_availablelogging)is_torchaudio_availablerequiresbackendscaa]tRt^)toRt.R OtR V3Rllt]!R R7R V3RlRll4tV3RlRlt Rt Vt V;t #) NeuCodecFeatureExtractora Constructs a NeuCodec feature extractor, which computes mel-filter bank features for the semantic encoder and padded audio for the acoustic encoder. This feature extractor inherits from [`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 80): The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 16000): The sample rate at which the audio files should be digitalized expressed in hertz (Hz). padding_value (`float`, *optional*, defaults to 1.0): The value that is used to fill the padding vectors for the mel spectrogram. hop_length (`int`, *optional*, defaults to 320): Number of audio samples encoded per frame. Equivalent to product of downsampling ratios. Needed for acoustic encoder input padding. c <\SV`!R RVRVRV/VBW@n\^VRR7VnRR.VPn^Vn^PVnRVn^Vn R#) feature_size sampling_rate padding_value)rrraudio padding_maskiN) super__init__ hop_lengthracoustic_encoder_paddermodel_input_namesstride num_mel_bins frame_length frame_shift)selfrrrrkwargs __class__s&&&&&,y/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/neucodec/feature_extraction_neucodec.pyr!NeuCodecFeatureExtractor.__init__@sz wlw-w_lwpvw%'?'( $ ;B>9R$$6 rc<V^8dQhRS[RS[S[,S[,RS[R,RS[RS[S[,R,RS[R,RS[R S[/#) rpadding max_lengthN truncationreturn_tensorsrdevicereturn)rboolstrrintr r)format __classdict__s"r& __annotate__%NeuCodecFeatureExtractor.__annotate__[sN N N o-N $J N  N j(4/ N TzN N  N r(c Ve>W`P8wd-\RV RVP RVP RV R2 4hM-\PRVPP R24\ V4pVF,p V P^8gK\R V P 24h \V4p VU u.uF0p \P!\P!V 4R!R R 7NK2 pp VPP\R V/4VVVVVP R R7p V P#R4p V R ,R",p.p\%V 4EFZpVPP\R VV,./4VP R R7pVR ,P'V4p\(P*P,P/VR#,VP0VP2VP, R,VP4VP, R,VPRRRRRR R^VP^,R7pVVP7^4, \P8!VP;^RR7R,4, pVP=V4EK] \RV/4pVPVVVVVP>VR R7pVR,pVPAR4pVP^,VP^,VP>,, pVRRV1R3,PCV VVP>,VP0VP>,4pVeTVRRV13,PCV VVP>,VP>4PER$R7PFp\RVRV RVRV/VR 7#uup i)%a Args: audio (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`): Numpy array or torch tensor with shape (num_channels, sequence_length). A list of such arrays or tensors can also be provided for a batch of inputs. 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). max_length (`int`, *optional*): Maximum length of the returned list and optionally padding length (see above). truncation (`bool`): Activates truncation to cut input sequences longer than *max_length* to *max_length*. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. sampling_rate (`int`, *optional*): The sample rate at which the `audio` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors. device (`str`, *optional*, defaults to `"cpu"`): Device for PyTorch tensors during mel-filter bank feature extraction. kwargs (*optional*): Remaining dictionary of keyword arguments that will be passed to the tokenizer or the feature extractor. Nz3The model corresponding to this feature extractor: z& was trained using a sampling rate of zD. 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.z6Expected input shape (channels, length) but got shape r)valuerpt)r,r-r+return_attention_maskpad_to_multiple_ofr.attention_maskNNN)r=r.ipoveyg ףp= ?TF) r r!r"sample_frequency window_typepreemphasis_coefficientremove_dc_offset use_log_fbank use_energydither snip_edgeslow_freq high_freq)unbiasedgHz>input_features)r+r,r-r=r<r.)dim input_valuesrinput_features_mask) tensor_type)r)r?Nr?i)$r ValueErrorloggerwarningr%__name__rndimshapelenFpadtorch as_tensorrrrpoprangeto torchaudio compliancekaldifbankr r!r"meansqrtvarappendrgetreshapeminvalues)r#rr+r,r-r.rr/r$example batch_sizea padded_inputsr padded_audio mel_featuresisingle_padded_audiowaveformfeaturesencoded_inputs padded_melaudio_spectrogramspectrogram_masktrimmed_framess&&&&&&&&, r&__call__!NeuCodecFeatureExtractor.__call__Zs^  $ 2 22 I$P**+,**+9]O1F3 NNVW[WeWeWnWnVop\\  #5)G||a #YZaZgZgYh!ijjZ HMMu!uq)6=uM4488 '5) *!!")#9 %(()9: $W-j9  z"A"&">">"B"Bgaz23#'??##C#  +7366v>H!,,2288E"!..!..1C1CCdJ ,,t/A/AADH!%!3!3#(,!%" ,,19H !8==#33uzz(,,q[_,B`cgBg7hhH    )1#2&'7&FGXX !!#{{") ''78%>>*:;*0037H7N7Nq7QTXT_T_7_`-a.!.CDLL $++5t7H7H4;;7V   ' O^O!34^t{{%BDKKP    "3%'7   '  INs;6O3c6<V^8dQhRS[S[S[3,/#)r*r0)dictr2r)r4r5s"r&r6r7sc3hr(c\P!VP4pVPPVR&VP RR4V#)feature_extractor_typerN)copydeepcopy__dict__r%rVr^)r#outputs& r&to_dict NeuCodecFeatureExtractor.to_dicts=t}}-+/>>+B+B'( ,d3 r()rr!r"rr r)rLrNrrO)Pi>g?i@)r\ra)TNFNNcpu) rV __module__ __qualname____firstlineno____doc__rrr r|r__static_attributes____classdictcell__ __classcell__)r%r5s@@r&rr)sJ&b4./N N 0N `r(r)ra)rtypingr audio_utilsrr!feature_extraction_sequence_utilsrfeature_extraction_utilsrutilsrr r r utils.import_utilsr r rar\torch.nn.functionalnn functionalrZ get_loggerrVrTr__all__rr(r&rs* 9I4MMC##   H % ?#E7E$EP & &r(