o piy@sdZddlmZddlmZddlmZmZmZm Z ddl Z ddl m Z mZddlmZmZddlmZdd lmZdd lmZdd lmZdd lmZmZdd lmZGdddeZGdddeZ dS)z"Voice activity detection pipelines)partial)Path)CallableOptionalTextUnionN) AnnotationSlidingWindowFeature)DetectionErrorRate DetectionPrecisionRecallFMeasure)Uniform) Inference) AudioFile)Pipeline) PipelineModel get_model)Binarizec@s&eZdZdZededefddZdS)OracleVoiceActivityDetectionz(Oracle voice activity detection pipelinefilereturncCs|d}|jdddS)a Return groundtruth voice activity detection Parameter --------- file : AudioFile Must provide a "annotation" key. Returns ------- hypothesis : `pyannote.core.Annotation` Speech regions annotationstringspeech) generatormodality) get_timelinesupport to_annotation)rrre/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/voice_activity_detection.pyapply/sz"OracleVoiceActivityDetection.applyN)__name__ __module__ __qualname____doc__ staticmethodrrr rrrrr,src seZdZdZ    ddededeedfdeeedfffd d Z d d Z d dZ ddZ dZ ddedeedefddZdeeeffddZddZZS)VoiceActivityDetectionaVoice activity detection pipeline Parameters ---------- segmentation : Model, str, or dict, optional Pretrained segmentation (or voice activity detection) model. Defaults to "pyannote/segmentation". See pyannote.audio.pipelines.utils.get_model for supported format. fscore : bool, optional Optimize (precision/recall) fscore. Defaults to optimizing detection error rate. 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 ---------------- onset, offset : float Onset/offset detection thresholds min_duration_on : float Remove speech regions shorter than that many seconds. min_duration_off : float Fill non-speech regions shorter than that many seconds. pyannote/segmentationFN segmentationfscoretoken cache_dirc st||_||_t|||d}dd|d<t|fi||_|jjr,d|_ |_ n t dd|_ t dd|_ t dd|_ t dd|_ dS)N)r*r+cSstj|dddS)NT)axiskeepdims)npmax)scoresrrrosz1VoiceActivityDetection.__init__..pre_aggregation_hookg?g?)super__init__r(r)rr _segmentationspecificationspowersetonsetoffsetr min_duration_onmin_duration_off)selfr(r)r*r+inference_kwargsmodel __class__rrr6_s     zVoiceActivityDetection.__init__cCs2|jdkr dddddS|jdkrddd St) Nr'g%C?gT㥛 ?g rh?gx&?r:r;r<r=zpyannote/segmentation-3.0.0r4)r<r=)r(NotImplementedErrorr>rrrdefault_parameterss  z)VoiceActivityDetection.default_parameterscCsdgS)NSPEECHrrErrrclassesszVoiceActivityDetection.classescCst|j|j|j|jd|_dS)z2Initialize pipeline with current set of parametersrCN)rr:r;r<r= _binarizerErrr initializes  z!VoiceActivityDetection.initializezcache/segmentation/inferencerhookrcCs|j||d}|jr&|j|vr||j}n|j|t|ddd}|||j<n |j|t|ddd}|d|||}|d|_|dd|DS)aApply voice activity 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 ------- speech : Annotation Speech regions. )rKr(NuricSsi|]}|dqS)rGr).0labelrrr sz0VoiceActivityDetection.apply..) setup_hooktrainingCACHED_SEGMENTATIONr7rrIrL rename_labelslabels)r>rrK segmentationsrrrrr s        zVoiceActivityDetection.applycCs|jr tdddStdddS)z'Return new instance of detection metricr4F)collar skip_overlap)r)r r rErrr get_metrics  z!VoiceActivityDetection.get_metriccCs|jrdSdS)Nmaximizeminimize)r)rErrr get_directionsz$VoiceActivityDetection.get_direction)r'FNN)N)r!r"r#r$rboolrrrr6rFrHrJrRrrrrr r r rXr[ __classcell__rrrArr&Bs,  ! .r&)!r$ functoolsrpathlibrtypingrrrrnumpyr/ pyannote.corerr pyannote.metrics.detectionr r pyannote.pipeline.parameterr pyannote.audior pyannote.audio.core.iorpyannote.audio.core.pipelinerpyannote.audio.pipelines.utilsrrpyannote.audio.utils.signalrrr&rrrrs