`i*dZddlmZmZddlZddlmZmZm Z ddl m Z ddl m Z ddlmZmZejeZGd d e Zd gZdS) z" Feature extractor class for CLVP )OptionalUnionN)mel_filter_bank spectrogramwindow_function)SequenceFeatureExtractor) BatchFeature) TensorTypeloggingcNeZdZdZddgZ dfd ZdejdejfdZ dde eje e e eje e e fde e dede e de e eefde ede ede e defdZxZS)ClvpFeatureExtractora! Constructs a CLVP 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. This class extracts log-mel-spectrogram features from raw speech using a custom numpy implementation of the `Short Time Fourier Transform` which should match pytorch's `torch.stft` equivalent. Args: feature_size (`int`, *optional*, defaults to 80): The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 22050): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). default_audio_length (`int`, *optional*, defaults to 6): The default length of raw audio in seconds. If `max_length` is not set during `__call__` then it will automatically be set to default_audio_length * `self.sampling_rate`. hop_length (`int`, *optional*, defaults to 256): Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients. chunk_length (`int`, *optional*, defaults to 30): The maximum number of chunks of `sampling_rate` samples used to trim and pad longer or shorter audio sequences. n_fft (`int`, *optional*, defaults to 1024): Size of the Fourier transform. padding_value (`float`, *optional*, defaults to 0.0): Padding value used to pad the audio. Should correspond to silences. mel_norms (`list` of length `feature_size`, *optional*): If `mel_norms` is provided then it will be used to normalize the log-mel spectrograms along each mel-filter. return_attention_mask (`bool`, *optional*, defaults to `False`): Whether to return the attention mask. If left to the default, it will return the attention mask. [What are attention masks?](../glossary#attention-mask) input_featuresattention_maskP"VNFc tjd |||| d| ||_||_||_||z|_|j|z|_||_||_||_ td|dzz|dd|dd|_ dS) N) feature_size sampling_rate padding_valuereturn_attention_maskrg@@slaneyhtk)num_frequency_binsnum_mel_filters min_frequency max_frequencyrnorm mel_scale) super__init__n_fft hop_length chunk_length n_samples nb_max_framesrdefault_audio_length mel_normsr mel_filters) selfrrr/r+r,r*rr0rkwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/clvp/feature_extraction_clvp.pyr)zClvpFeatureExtractor.__init__Gs  %''"7        $(% 5!^z9*$8!"* EQJ/( '   waveformreturnc "t|t|jd|j|jd|jd}t jt j|dd}|j&|t j |jdddfz }|S)z This method first computes the log-mel spectrogram of the provided audio then applies normalization along the each mel-filterbank, if `mel_norms` is provided. hanng@N) frame_lengthr+powerr1log_melgh㈵>)a_mina_max) rrr*r+r1nplogclipr0array)r2r7log_specs r5_np_extract_fbank_featuresz/ClvpFeatureExtractor._np_extract_fbank_featuresms   DJ / /(   6"'($dCCCDD > %"(4>":":111d7"CCHr6T max_length raw_speechr truncationpad_to_multiple_ofreturn_tensorsrpaddingc 4|<|jkr0tdjjdjdjd|d n(tdjjdt |tjot|j d k} | r*t|j d krtd | pHt |ttfo,t |d tjttf} | r d |D}n| s;t |tjs!tj |tj}n^t |tjrD|jtjtjur|tj}| stj |gjg}t'd|i} |jjzn|}| |||||} | dd d d }fd|d D}t |d trd|D| d<n|| d<| |S)a `ClvpFeatureExtractor` is used to extract various voice specific properties such as the pitch and tone of the voice, speaking speed, and even speaking defects like a lisp or stuttering from a sample voice or `raw_speech`. First the voice is padded or truncated in a way such that it becomes a waveform of `self.default_audio_length` seconds long and then the log-mel spectrogram is extracted from it. Args: raw_speech (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`): The sequence or batch of sequences to be padded. 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. Must be mono channel audio, not stereo, i.e. single float per timestep. sampling_rate (`int`, *optional*): The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition pipeline. truncation (`bool`, *optional*, default to `True`): Activates truncation to cut input sequences longer than *max_length* to *max_length*. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. return_attention_mask (`bool`, *optional*, defaults to `True`): Whether to return the attention mask. If left to the default, it will return the attention mask. [What are attention masks?](../glossary#attention-mask) 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. padding_value (`float`, *optional*, defaults to 0.0): The value that is used to fill the padding values / vectors. max_length (`int`, *optional*): The maximum input length of the inputs. Nz3The model corresponding to this feature extractor: z& was trained using a sampling rate of zI. Please make sure that the provided `raw_speech` 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.rrz2Only mono-channel audio is supported for input to rcZg|](}tj|gtjj)S)dtype)r@asarrayfloat32T).0speechs r5 z1ClvpFeatureExtractor.__call__..s.\\\v"*fXRZ@@@B\\\r6rOr)rKrFrHrIrctg|]4}|tj5Sr')rEastyper@rR)rTr7r2s r5rVz1ClvpFeatureExtractor.__call__..sD   MUD + +H 5 5 < .s".a.a.awrz'/B/B.a.a.ar6)r ValueErrorr4__name__loggerwarning isinstancer@ndarraylenshapelisttuplerQrRrPfloat64rXrSr r/padget transposeconvert_to_tensors)r2rGrrHrIrJrrKrFr3is_batched_numpy is_batchedbatched_speech padded_inputsrs` r5__call__zClvpFeatureExtractor.__call__sf  $ 222 W$.JaWW)-);WW)-);WWFSWWW3 NN\W[WeWn\\\    &j"*==[#jFVBWBWZ[B[  ZJ$4 5 5 9 9XRVXXYY Y% zD%= 1 1 lz*Q-RTR\^ceiQj7k7k   7\\Q[\\\JJ 7Jz2:$F$F 7JbjAAAJJ  BJ / / 7J4DQSQ[H\H\4\4\#**2:66J 6*j\2245J%'7&DEEGQGYT.1CCC_i  !!1"7 !  '**+;<<FFq!QOO    YghiYj    nQ' . . =.a.aR`.a.a.aM* + +.rs #"""""""HHHHHHHHHHIIIIII444444((((((((  H % %M@M@M@M@M@3M@M@M@` " "r6