o 9wi- @s(dZddlmZddlmZddlmZmZddlZddl Z ddl Z ddl mZmZmZmZddlmZe   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)pairwise?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 r rrX/home/ubuntu/sommelier/.venv/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 r r batch_size num_frameson off_or_onwell_defined_idxsame_assamplesrrrbinarize_ndarrayNs2     r4c 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 r r?z k f -> f kzc f k -> (c k) f)cr6r7z(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 r rr. num_classesr; binarized num_chunksrrr binarize_swfs@      rDc 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 r min_duration_onmin_duration_off pad_onset pad_offsetcs6t||_|p ||_||_||_||_||_dSN)super__init__r r rIrJrGrH)selfr r rGrHrIrJ __class__rrrMs   zBinarize.__init__rreturncsn|jj\}}|jfddt|D}t}t|jjD]f\}}|jdur(|n|j|}|d} |d|jk} t |dd|ddD]*\} } | rf| |j kret | |j | |j } ||| |f<| } d} qE| |jkro| } d} qE| rt | |j | |j } ||| |f<q|j dks|j dks|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__..NrrFTrF)collar)r;rr?ranger enumerater%labelsr zipr rrIrJrHsupportrGr' itertracksduration)rNrr.rA timestampsactiver7k_scoreslabelstart is_activetyregionsegmenttrackrrUr__call__s@ "       zBinarize.__call__)r NrFrFrFrF) __name__ __module__ __qualname____doc__floatrrMrrrj __classcell__rrrOrrEs,rEcs>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 r8alpha min_durationcstt|||_||_dSrK)rLrqrMrrrs)rNrrrsrOrrrMKs 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$g|]}|jkr|jqSr)rrrRrSrVrrNrrrms$z!Peak.__call__..) dimensionr@lenr?stepmaxintrrintrsscipysignal argrelmaxr&hstackrcendr rYr radd) rNrr. precisionrtindices peak_time boundaries segmentationrTrcrrhrrurrjTs    z Peak.__call__)r r8) rkrlrmrnrorMrrjrprrrOrrq@s  rq)r NN)rn functoolsr itertoolsrtypingrrr=numpyr scipy.signalr| pyannote.corerrrr pyannote.core.utils.generatorsr ror!r#rregisterr4rDrErqrrrrs`    ! A >q