:jA2ddlZddlmZmZmZmZmZmZddlZ ddl m Z ddl m Z mZmZmZddlmZddlmZddlmZmZd Zd Zd Zd Zd ZdZGddeZy)N)UnionIterableOptionalTupleListDict) ArrayLike)Segment AnnotationSlidingWindowFeatureTimeline) BaseMetric) det_curve)MetricComponentsDetailstargetspeaker_latency spk_scoreabsolute_latency abs_scorescorec BeZdZdZedefdZdeeeffdZ dde e de e ffd Z de fd Zd ed eed eedefd Zd eeefd eed eedefdZd eeefdeeeeeeffdefdZedZedZ ddedededefdZxZS)LowLatencySpeakerSpottingaEvaluation of low-latency speaker spotting (LLSS) systems LLSS systems can be evaluated in two ways: with fixed or variable latency. * When latency is fixed a priori (default), only scores reported by the system within the requested latency range are considered. Varying the detection threshold has no impact on the actual latency of the system. It only impacts the detection performance. * In variable latency mode, the whole stream of scores is considered. Varying the detection threshold will impact both the detection performance and the detection latency. Each trial will result in the alarm being triggered with a different latency. In case the alarm is not triggered at all (missed detection), the latency is arbitrarily set to the value one would obtain if it were triggered at the end of the last target speech turn. The reported latency is the average latency over all target trials. Parameters ---------- latencies : float iterable, optional Switch to fixed latency mode, using provided `latencies`. Defaults to [1, 5, 10, 30, 60] (in seconds). thresholds : float iterable, optional Switch to variable latency mode, using provided detection `thresholds`. Defaults to fixed latency mode. returncy)NzLow-latency speaker spotting)clss n/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/pyannote/metrics/spotting.py metric_namez%LowLatencySpeakerSpotting.metric_nameLs-c ddiS)Nrgr)selfs rmetric_componentsz+LowLatencySpeakerSpotting.metric_componentsPs "~r! thresholds latenciesct|||gd}| | td|tj||_|tj|}||_y)N)r <z3One must choose between fixed and variable latency.)super__init__ ValueErrornpsortr%r&)r#r%r& __class__s rr-z"LowLatencySpeakerSpotting.__init__Ssp   )"3*I  !i&;EG G  ! ggj1DO   *I"r!detailcy)Nr)r#r2s rcompute_metricz(LowLatencySpeakerSpotting.compute_metricgsr! reference timestampsscoresc |sFd}tj|tjt|jdfz}|}nd}tj |Dcgc]}|j c}}g}g}ttj||j|jD]\} } || j|| | z z } tj|| } | dkrtjj } ntj|d| } |j| |dj| z} tj|| } | dkrtjj } ntj|d| } |j| tj|j!d}tj|j!d}t"|t$|jt&|t(|jt*|iScc}w#t$rtj|} Y'wxYw)NFrTrr)r/maxoneslenr&cumsumdurationzip searchsortedendsys float_info IndexErrorappendstartarrayreshapeSPOTTING_TARGETSPOTTING_SPK_LATENCYSPOTTING_SCORESPOTTING_ABS_LATENCYSPOTTING_ABS_SCORE)r#r5r6r7 target_trialrrsegmenttotalilatencytup_toss r_fixed_latencyz(LowLatencySpeakerSpotting._fixed_latencyjs Lv#dnn2Eq1I)JJI!I LIIyIGw//IJEII""//%"H"&..2 $ 7 '!! ((E!Hw,>?AOOJ:Eqy ^^///FF6&5>2  #aL&&0 A619+++Avfu~.A  #3 $6+33G1 Q/%.^^F%;%D%D%F"&)!V__&= # >**''/88AT__, (((1$//(BD $1OOIadOTBDE L!ww'7vN  ggowVLO!Q'H+4*:*:*<*E*E hY '*3););)=OXI &!L "vv  ffO \ "2 / BFF6N   9Ds'I hypothesisc t|tr |Dcgc]\}}|j|f}}}t|\}}|j|j |||S|j |||Scc}}w)z Parameters ---------- reference : Timeline or Annotation hypothesis : SlidingWindowFeature or (time, score) iterable )r^r rBr@r&rnrW)r#r5rorfwindowvaluer6r7s rcompute_componentsz,LowLatencySpeakerSpotting.compute_componentssz j"6 7CMN-&%6::u-NJN *- F >> !)))ZH H&&y*fE EOsA.c|Dcgc]\}}|tr |t}}}tj|dScc}}wNr)axis)rJrMr/nanmeanr#rktrialr&s rrz*LowLatencySpeakerSpotting.absolute_latencyFAE0XQo./00 0zz)!,,0<c|Dcgc]\}}|tr |t}}}tj|dScc}}wru)rJrKr/rwrxs rrz)LowLatencySpeakerSpotting.speaker_latencyrzr{ cost_misscost_fa prior_targetreturn_latencyc$|jEtj|Dcgc]\}}|tc}}}tj|Dcgc]\}}|tc}}}t ||d\} } } } | ddd| ddd| ddd} } } || z|z|| zd|z zz} |rtj | |jd}tj| |d } tj| |d } tj| |d } tj| |d } | | | | | |j|jfS| | | | | fStj|Dcgc]\}}|tc}}}tj|Dcgc]\}}|tc}}}tj|Dcgc]\}}|tc}}}i}||d jD]z\}}i||<t|jD]X\}}t ||dd|fd\} } }} | ddd| ddd|ddd}} } || z|z|| zd|z zz} || | | | f|||<Z||Scc}}wcc}}wcc}}wcc}}wcc}}w) aDET curve Parameters ---------- cost_miss : float, optional Cost of missed detections. Defaults to 100. cost_fa : float, optional Cost of false alarms. Defaults to 1. prior_target : float, optional Target trial prior. Defaults to 0.5. return_latency : bool, optional Set to True to return latency. Has no effect when latencies are given at initialization time. Returns ------- thresholds : numpy array Detection thresholds fpr : numpy array False alarm rate fnr : numpy array False rejection rate eer : float Equal error rate cdet : numpy array Cdet cost function speaker_latency : numpy array absolute_latency : numpy array Speaker and absolute latency when return_latency is set to True. NF) distancesr:g?left)siderZr[)speakerabsolute)r&r/rHrJrLrrAr%rdrrSPOTTING_SPK_SCORErNitemsr`)r#r}r~rrrkryy_truer7fprfnrr%eercdetrl spk_scores abs_scoresresultkeyrRrSthetas rrz#LowLatencySpeakerSpotting.det_curvesH >> !XXdK(!Uu_5KLFXXTJEu^4JKF(1&&E(R %Cj##&tt9c$B$iDbD9IjCs?\1S=B$567D//*dooFSWWZvF ggc78ggc78wwtW6:!3S$++T-B-BCC"3S$66XXdK(!Uu_5KLFT"RE5);#<"RSJT"RE5);#<"RSJF+5,6 88= F V!s "+DNN";FJAw+4VVAqD\?D,F(CeS&)$B$iTrTE$B$KeC$s?\9"S=B,=>?D+0#sC+EF3K( F FMULJ.L"R"RsI4 I: 'J J J )NN)drg{Gz?F) __name__ __module__ __qualname____doc__ classmethodstrr rfloatr$rr r-rr4r rrrWrr rnr rrrspropertyrrboolr __classcell__)r1s@rrr0s6.C..4U #34826#%i0#$Y/#(%58 8 #';8 8rsU: ??"MM,,( ) ^ ^r!