o 9wi)@sdZddlmZddlmZmZmZmZddlZ ddl m Z m Z m Z ddlmZddlmZddlmZmZdd lmZdd lmZdd lmZmZmZdd lmZmZdd l m!Z!GdddeeZ"dS)zResegmentation pipeline)partial)CallableOptionalTextUnionN) AnnotationSegmentSlidingWindowFeature)GreedyDiarizationErrorRate)Uniform) InferenceModel) AudioFile)Pipeline) PipelineModelSpeakerDiarizationMixin get_model) mae_cost_func permutate)binarizec seZdZdZ    ddededeedeedfffdd Z d d Z d d Z dZ  dde deedeedefddZdefddZZS)ResegmentationaResegmentation pipeline This pipeline relies on a pretrained segmentation model to improve an existing diarization hypothesis. Resegmentation is done locally by sliding the segmentation model over the whole file. For each position of the sliding window, we find the optimal mapping between the input diarization and the output of the segmentation model and permutate the latter accordingly. Permutated local segmentations scores are then aggregated over time and postprocessed using hysteresis thresholding. It can also be used with `diarization` set to "annotation" to find a good estimate of optimal values for `onset`, `offset`, `min_duration_on`, and `min_duration_off` for any speaker diarization pipeline based on the `segmentation` model. Parameters ---------- segmentation : Model, str, or dict, optional Pretrained segmentation model. Defaults to "pyannote/segmentation". See pyannote.audio.pipelines.utils.get_model for supported format. diarization : str, optional File key to use as input diarization. Defaults to "diarization". der_variant : dict, optional Optimize for a variant of diarization error rate. Defaults to {"collar": 0.0, "skip_overlap": False}. This is used in `get_metric` when instantiating the metric: GreedyDiarizationErrorRate(**der_variant). 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` Hyper-parameters ---------------- onset, offset : float Onset/offset detection thresholds min_duration_on : float Remove speaker turn shorter than that many seconds. min_duration_off : float Fill same-speaker gaps shorter than that many seconds. pyannote/segmentation diarizationN segmentation der_variantuse_auth_tokencst||_||_t||d}t||_|j|_t |j j |_ |p'ddd|_ tdd|_tdd|_tdd|_tdd|_tdd|_dS)N)rF)collar skip_overlapg?g?)super__init__rrrr _segmentationaudio_audiolenspecificationsclasses _num_speakersrr warm_uponsetoffsetmin_duration_onmin_duration_off)selfrrrrmodel __class__d/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/pyannote/audio/pipelines/resegmentation.pyr Us       zResegmentation.__init__cCs |jdkr ddddddSt)Nrg?gQ?gbX9?g)\(?gJ +?)r(r)r*r+r,)rNotImplementedErrorr-r1r1r2default_parametersws z!Resegmentation.default_parameterscCst)N)r3r4r1r1r2r&szResegmentation.classeszcache/segmentation/inferencefilehookreturncCs:|j||d}|jr&|j|vr||j}n|j|t|ddd}|||j<n |j|t|ddd}|d|t||j|jdd}|j||jj j |j |j fd}|d||pZ||j }|j td|j||jj|jj j d }|d |tj||j |j fd}|d ||jj\}}||jkrt|jd d d ||jff|_n||jkrt|jd d |j|ff|_|j}t|jtj} | j\}} }t|D]\} \} } || tjd| f}t|| td\\| | <}qt | |j!} |d| |"| |}|j#||j$|j%d}|d|_&d|vr|dr|'|d|}|S)aaApply speaker diarization Parameters ---------- file : AudioFile Processed file. diarization : Annotation, optional Input diarization. Defaults to file[self.diarization]. 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 ------- diarization : Annotation Speaker diarization )r7rNF)r)r* initial_state)r(speaker_countingr)support resolutionz@resegmentation/originalz@resegmentation/trim)rrr) cost_funcz@resegmentation/permutated)r+r,uri annotation)( setup_hooktrainingCACHED_SEGMENTATIONr!rrr)r* speaker_countr.receptive_fieldr(r discretizerr# get_durationstepr trimdatashaper'nppad full_likeNAN enumeratecropnewaxisrrr sliding_windowto_diarization to_annotationr+r,r>optimal_mapping)r-r6rr7 segmentationsbinarized_segmentationscount_ num_speakerspermutated_segmentations num_framescchunkrlocal_diarizationdiscrete_diarizationresegmentationr1r1r2applys                  zResegmentation.applycCstdi|jS)Nr1)r rr4r1r1r2 get_metric szResegmentation.get_metric)rrNN)NN)__name__ __module__ __qualname____doc__rrrdictrr r5r&rBrrrrbr rc __classcell__r1r1r/r2r-s>) "  r)#rg functoolsrtypingrrrrnumpyrK pyannote.corerrr pyannote.metrics.diarizationr pyannote.pipeline.parameterr pyannote.audior r pyannote.audio.core.iorpyannote.audio.core.pipelinerpyannote.audio.pipelines.utilsrrr pyannote.audio.utils.permutationrrpyannote.audio.utils.signalrrr1r1r1r2s