o pit- @s,dZddlmZddlmZddlmZmZddlZddl Z ddl Z ddl mZmZmZmZddlmZmZe   dd ed eed eeee jffd d Zej   dde jd ed eed eeee jffddZej   dded ed eed eefddZGdddZGdddZdS)z # Signal processing )singledispatch) zip_longest)OptionalUnionN) AnnotationSegmentSlidingWindowFeatureTimeline)pairwisestring_generator?onsetoffset initial_statecCstd)a(Batch) hysteresis thresholding Parameters ---------- scores : numpy.ndarray or SlidingWindowFeature (num_chunks, num_frames, num_classes)- or (num_frames, num_classes)-shaped scores. onset : float, optional Onset threshold. Defaults to 0.5. offset : float, optional Offset threshold. Defaults to `onset`. initial_state : np.ndarray or bool, optional Initial state. Returns ------- binarized : same as scores Binarized scores with same shape and type as scores. Reference --------- https://stackoverflow.com/questions/23289976/how-to-find-zero-crossings-with-hysteresis z=scores must be of type numpy.ndarray or SlidingWindowFeatures)NotImplementedError)scoresr rrrO/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/utils/signal.pybinarize,srrc Cs6|p|}|j\}}t|}|dur!|dddfd||k}n%t|tr1|tj|ftd}nt|tjrF|j|fks?J|jtksFJt||dfj }||k}||k|B}t t t dd|Ddd ij }|j swtj|td|BStj|dd } tt||dfj } t| || || | dff|S) a(Batch) hysteresis thresholding Parameters ---------- scores : numpy.ndarray (num_frames, num_classes)-shaped scores. onset : float, optional Onset threshold. Defaults to 0.5. offset : float, optional Offset threshold. Defaults to `onset`. initial_state : np.ndarray or bool, optional Initial state. Returns ------- binarized : same as scores Binarized scores with same shape and type as scores. Nrr )dtypecSsg|] }t|dqS)r)npnonzero).0oonrrr sz$binarize_ndarray.. fillvalue)axis)shaper nan_to_num isinstanceboolonesndarrayrtileTarraylistrsize zeros_likecumsumarangewhere) rr rr batch_size num_frameson off_or_onwell_defined_idxsame_assamplesrrrbinarize_ndarrayNs2     r5c Cs|p|}|jjdkr1|jj\}}tj|jd||d}t||||d}tdtj|d||d|jS|jjdkra|jj\}}}tj|jd|||d }t||||d}tdtj|d |||d |jStd ) a(Batch) hysteresis thresholding Parameters ---------- scores : SlidingWindowFeature (num_chunks, num_frames, num_classes)- or (num_frames, num_classes)-shaped scores. onset : float, optional Onset threshold. Defaults to 0.5. offset : float, optional Offset threshold. Defaults to `onset`. initial_state : np.ndarray or bool, optional Initial state. Returns ------- binarized : same as scores Binarized scores with same shape and type as scores. z f k -> k f)fk)r rr?z k f -> f kzc f k -> (c k) f)cr7r8z(c k) f -> c f kz[Shape of scores must be (num_chunks, num_frames, num_classes) or (num_frames, num_classes).) datandimreinops rearrangerrsliding_window ValueError) rr rrr/ num_classesr< binarized num_chunksrrr binarize_swfs@      rEc s^eZdZdZ      ddedeededed ed ef fd d Zd edefddZ Z S)BinarizeaBinarize detection scores using hysteresis thresholding Parameters ---------- onset : float, optional Onset threshold. Defaults to 0.5. offset : float, optional Offset threshold. Defaults to `onset`. min_duration_on : float, optional Remove active regions shorter than that many seconds. Defaults to 0s. min_duration_off : float, optional Fill inactive regions shorter than that many seconds. Defaults to 0s. pad_onset : float, optional Extend active regions by moving their start time by that many seconds. Defaults to 0s. pad_offset : float, optional Extend active regions by moving their end time by that many seconds. Defaults to 0s. Reference --------- Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of RNN-based Voice Activity Detection", InterSpeech 2015. r Nr rmin_duration_onmin_duration_off pad_onset pad_offsetcs6t||_|p ||_||_||_||_||_dSN)super__init__r rrJrKrHrI)selfr rrHrIrJrK __class__rrrNs   zBinarize.__init__rreturncs||jj\}}|jfddt|D}t}t}t|jjD]j\}}|jdur+|n|j|} t |} |d} |d|j k} t |dd|ddD]*\} }| rm||j krlt | |j| |j}| ||| f<| } d} qL||j krv| } d} qL| rt | |j| |j}| ||| f<q |jdks|jdks|jdkr|j|jd }|jdkrt|D]\}} |j|jkr||| f=q|S) zBinarize detection scores Parameters ---------- scores : SlidingWindowFeature Detection scores. Returns ------- active : Annotation Binarized scores. csg|]}|jqSr)middleriframesrrrsz%Binarize.__call__..NrrFTrG)collar)r<rr@rangerr enumerater&labelsnextr ziprrrJrKrIsupportrHr( itertracksduration)rOrr/rB timestampsactivetrack_generatorr8k_scoreslabeltrackstart is_activetyregionsegmentrrVr__call__sD "       zBinarize.__call__)r NrGrGrGrG) __name__ __module__ __qualname____doc__floatrrNrrrm __classcell__rrrPrrFs,rFcs>eZdZdZ  d dedeffdd Zdefd d ZZS) PeakzPeak detection Parameters ---------- alpha : float, optional Peak threshold. Defaults to 0.5 min_duration : float, optional Minimum elapsed time between two consecutive peaks. Defaults to 1 second. r r9alpha min_durationcstt|||_||_dSrL)rMrtrNrurv)rOrurvrPrrrNLs z Peak.__init__rc sjdkr tdt}jj}tdttj |}t j j dd|dd}t fdd|D}tdjg||jgg}t}tt|D]\} \} } t| | } || qU|S)zPeak detection Parameter --------- scores : SlidingWindowFeature Detection scores. Returns ------- segmentation : Timeline Partition. rz$Peak expects one-dimensional scores.N)orderrcs$g|]}|jkr|jqSr)rurSrTrWrrOrrrns$z!Peak.__call__..) dimensionrAlenr@stepmaxintrrintrvscipysignal argrelmaxr'hstackrgendr rZr radd) rOrr/ precisionrwindices peak_time boundaries segmentationrUrgrrlrrxrrmUs    z Peak.__call__)r r9) rnrorprqrrrNrrmrsrrrPrrtAs  rt)r NN)rq functoolsr itertoolsrtypingrrr>numpyr scipy.signalr pyannote.corerrrr pyannote.core.utils.generatorsr r rrr"r$rregisterr5rErFrtrrrrs`   ! A >r