o pi!@sddlmZddlmZddlmZmZmZmZddl m Z ddl m Z ddl mZddlmZddlmZmZdd lmZdd lmZmZd d lmZd dlmZmZGdddeZdS))partial)Path)CallableOptionalTextUnion) Inference) AudioFile)Pipeline)MacroAverageFMeasure) AnnotationSlidingWindowFeature)IdentificationErrorRate ParamDictUniform)Binarize) PipelineModel get_modelcseZdZdZ     ddeedededeedfdee edff fd d Z d d Z d dZ dZ ddedeedefddZdeeeffddZddZZS)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`. NF segmentationfscoreshare_min_durationtoken cache_dirc st|dur td||_||_||_t|||d}|jj|_ t |fi||_ |jrJt dd|_ t dd|_tdidd|j D|_dStdidd|j D|_dS) NzMMultiLabelSegmentation pipeline must be provided with a `segmentation` model.)rr@cSs&i|]}|ttddtdddqS)r?)onsetoffsetr.0labelr%W/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/multilabel.py msz3MultiLabelSegmentation.__init__..c Ss6i|]}|ttddtddtddtdddqS)rrrr r!min_duration_onmin_duration_offrr"r%r%r&r'wsr%)super__init__ ValueErrorrrrrspecificationsclasses_classesr _segmentationrr)r*r thresholds)selfrrrrrinference_kwargsmodel __class__r%r&r,Ms0       zMultiLabelSegmentation.__init__cCs|jSN)r0r3r%r%r&r/szMultiLabelSegmentation.classescsfddjD_dS)z2Initialize pipeline with current set of parametersc s^i|]+}|tj|dj|djsj|dnjjs(j|dnjdqS)r r!r)r*r()rr2rr)r*r"r9r%r&r's  z5MultiLabelSegmentation.initialize..N)r0 _binarizer9r%r9r& initializes  z!MultiLabelSegmentation.initializezcache/segmentationfilehookreturnc Cs|j||d}|jr&|j|vr||j}n|j|t|ddd}|||j<n |j|t|ddd}|d|t|dd}t|jD],\}}t|j dd||df|j }|j ||}| |j t||ddqB|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_SEGMENTATIONr1rr enumerater0r datasliding_windowr:update rename_labelsdictfromkeyslabels) r3r<r= segmentations detectionir$label_segmentationlabel_annotationr%r%r&applys0      zMultiLabelSegmentation.applycCs|jr t|jdStS)z,Return new instance of identification metric)r/)rr r0rr9r%r%r& get_metrics z!MultiLabelSegmentation.get_metriccCs|jrdSdS)Nmaximizeminimize)rr9r%r%r& get_directionsz$MultiLabelSegmentation.get_direction)NFFNNr8)__name__ __module__ __qualname____doc__rrboolrrrr,r/r;rCr rr rQr rrRrU __classcell__r%r%r6r&r,s0"  6>rN) functoolsrpathlibrtypingrrrrpyannote.audiorpyannote.audio.core.ior pyannote.audio.core.pipeliner pyannote.audio.utils.metricr pyannote.corer r pyannote.metrics.identificationrpyannote.pipeline.parameterrr utils.signalrutilsrrrr%r%r%r&s