o 9wi!@sddlmZddlmZmZmZmZddlmZm Z ddl m Z ddl m Z mZddlmZddlmZddlmZdd lmZd d lmZd d lmZmZGdddeZdS))partial)CallableOptionalTextUnion) AnnotationSlidingWindowFeature)IdentificationErrorRate ParamDictUniform) Inference) AudioFile)Pipeline)MacroAverageFMeasure)Binarize) PipelineModel get_modelc seZdZdZ    ddeedededeedfffdd Z d d Z d d Z dZ dde deedefddZdeeeffddZddZZS)MultiLabelSegmentationaPGeneric 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. use_auth_token : str, optional When loading private huggingface.co models, set `use_auth_token` to True or to a string containing your hugginface.co authentication token that can be obtained by running `huggingface-cli login` 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_durationuse_auth_tokenc st|dur td||_||_||_t||d}|jj|_ t |fi||_ |jrIt dd|_ t dd|_tdidd|j D|_dStdidd|j D|_dS) NzMMultiLabelSegmentation pipeline must be provided with a `segmentation` model.)r@cSs&i|]}|ttddtdddqS)r?)onsetoffsetr .0labelr#`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/pyannote/audio/pipelines/multilabel.py lsz3MultiLabelSegmentation.__init__..c Ss6i|]}|ttddtddtddtdddqS)rrrrrmin_duration_onmin_duration_offr r r#r#r$r%vsr#)super__init__ ValueErrorrrrrspecificationsclasses_classesr _segmentationr r'r(r thresholds)selfrrrrinference_kwargsmodel __class__r#r$r*Ls0        zMultiLabelSegmentation.__init__cCs|jSN)r.r1r#r#r$r-szMultiLabelSegmentation.classescsfddjD_dS)z2Initialize pipeline with current set of parametersc s^i|]+}|tj|dj|djsj|dnjjs(j|dnjdqS)rrr'r(r&)rr0rr'r(r r7r#r$r%s  z5MultiLabelSegmentation.initialize..N)r. _binarizer7r#r7r$ 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_SEGMENTATIONr/rr enumerater.rdatasliding_windowr8update rename_labelsdictfromkeyslabels) r1r:r; segmentations detectionir"label_segmentationlabel_annotationr#r#r$applys0      zMultiLabelSegmentation.applycCs|jr t|jdStS)z,Return new instance of identification metric)r-)rrr.r r7r#r#r$ get_metrics z!MultiLabelSegmentation.get_metriccCs|jrdSdS)Nmaximizeminimize)rr7r#r#r$ get_directionsz$MultiLabelSegmentation.get_direction)NFFNr6)__name__ __module__ __qualname____doc__rrboolrrr*r-r9rArrrrOrr rPrS __classcell__r#r#r4r$r+s*" 6>rN) functoolsrtypingrrrr pyannote.corerrpyannote.metrics.identificationr pyannote.pipeline.parameterr r pyannote.audior pyannote.audio.core.iorpyannote.audio.core.pipelinerpyannote.audio.utils.metricr utils.signalrutilsrrrr#r#r#r$s