i!ddlmZddlmZddlmZmZmZmZddl m Z ddl m Z ddl mZddlmZddlmZmZdd lmZdd lmZmZd d lmZd dlmZmZGddeZy))partial)Path)CallableOptionalTextUnion) Inference) AudioFile)Pipeline)MacroAverageFMeasure) AnnotationSlidingWindowFeature)IdentificationErrorRate) ParamDictUniform)Binarize) PipelineModel get_modelceZdZdZ ddeedededeedfdee edff fd Z d Z d Z d Z dd ed eedefdZdeeeffdZdZxZS)MultiLabelSegmentationa7Generic multi-label segmentation Parameters ---------- segmentation : Model, str, or dict Pretrained multi-label segmentation model. See pyannote.audio.pipelines.utils.get_model for supported format. fscore : bool, optional Optimize for average (precision/recall) fscore, over all classes. Defaults to optimizing identification error rate. share_min_duration : bool, optional If True, `min_duration_on` and `min_duration_off` are shared among labels. token : str or bool, optional Huggingface token to be used for downloading from Huggingface hub. cache_dir: Path or str, optional Path to the folder where files downloaded from Huggingface hub are stored. inference_kwargs : dict, optional Keywords arguments passed to Inference. Hyper-parameters ---------------- Each {label} of the segmentation model is assigned four hyper-parameters: onset, offset : float Onset/offset detection thresholds min_duration_on : float Remove {label} regions shorter than that many seconds. Shared between labels if `share_min_duration` is `True`. min_duration_off : float Fill non-{label} regions shorter than that many seconds. Shared between labels if `share_min_duration` is `True`. N segmentationfscoreshare_min_durationtoken cache_dirc t || td||_||_||_t |||}|jj|_ t|fi||_ |j rktdd|_ tdd|_tdi|jDcic]$}|ttddtdd&c}|_ytdi|jDcic]:}|ttddtddtddtdd<c}|_ycc}wcc}w) NzMMultiLabelSegmentation pipeline must be provided with a `segmentation` model.)rrgg@g?)onsetoffsetrr min_duration_onmin_duration_off)super__init__ ValueErrorrrrrspecificationsclasses_classesr _segmentationrr"r#r thresholds) selfrrrrrinference_kwargsmodellabel __class__s x/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/pyannote/audio/pipelines/multilabel.pyr&zMultiLabelSegmentation.__init__Msj   _ ) "4,eyI,,44 &uA0@A  " "#*3#4D $+C$5D !' "&  "/ 9%c3/&sC0"/ DO( "&"/ 9%c3/&sC0(/S(9)0c):  "/ DOs /)E 8?E c|jSN)r*r-s r2r)zMultiLabelSegmentation.classess }}c H|jDcic]}|t|j|d|j|d|js|j|dn |j|js|j|dn |j c}|_ycc}w)z2Initialize pipeline with current set of parametersrr r"r#r!N)r*rr,rr"r# _binarize)r-r0s r2 initializez!MultiLabelSegmentation.initializes" ' 8ooe,W5u-h7 22OOE*+<=-- 22OOE*+=>..  '  sBBzcache/segmentationfilehookreturnc |j||}|jrL|j|vr||j}nL|j|t |dd}|||j<n|j|t |dd}|d|t |d}t |jD]\}}t|jdd||dzf|j}|j||}|j|jtj|j!|d|S) aApply multi-label detection Parameters ---------- file : AudioFile Processed file. hook : callable, optional Callback called after each major steps of the pipeline as follows: hook(step_name, # human-readable name of current step step_artefact, # artifact generated by current step file=file) # file being processed Time-consuming steps call `hook` multiple times with the same `step_name` and additional `completed` and `total` keyword arguments usable to track progress of current step. Returns ------- detection : Annotation Detected regions. )r;rNuri)r>rF)copy) setup_hooktrainingCACHED_SEGMENTATIONr+rr enumerater*rdatasliding_windowr8update rename_labelsdictfromkeyslabels) r-r:r; segmentations detectionir0label_segmentationlabel_annotations r2applyzMultiLabelSegmentation.applysU.t$/ ==''4/ $T%=%= > $ 2 2wt^TB!3! 2?T--.262D2D74>3E3M ^]+4;/ !$--0HAu!5""1a!a%i<0-2N2N" ,A4>>%+@AS+T     ..MM"2"9"9";UC%/ 1r6cZ|jrt|jStS)z,Return new instance of identification metric)r))rr r*rr5s r2 get_metricz!MultiLabelSegmentation.get_metrics# ;;' > >&((r6c|jryy)Nmaximizeminimize)rr5s r2 get_directionz$MultiLabelSegmentation.get_directions ;;r6)NFFNNr4)__name__ __module__ __qualname____doc__rrboolrrrr&r)r9rBr rr rPr rrRrV __classcell__)r1s@r2rr,sD15#(#'-1 3}-33! 3 T4Z 3 tT)* 3l (/<)<8H+=<<|)E"68O"OP)r6rN) functoolsrpathlibrtypingrrrrpyannote.audior pyannote.audio.core.ior pyannote.audio.core.pipeliner pyannote.audio.utils.metricr pyannote.corer rpyannote.metrics.identificationrpyannote.pipeline.parameterrr utils.signalrutilsrrrr$r6r2ris<822$,1<:C:#+yXyr6