o 9wid@sdZddlZddlZddlZddlZddlZddlmZmZm Z m Z ddl Z ddl Z ddlmZddlmZmZddlmZddlmZmZddlmZmZmZmZdd lmZdd lm Z dd l!m"Z"dd l#m$Z$m%Z%m&Z&dd l'm(Z(dde)fddZ*Gddde%eZ+dS)zSpeaker diarization pipelinesN)CallableOptionalTextUnion) rearrange) AnnotationSlidingWindowFeature)GreedyDiarizationErrorRate) ParamDictUniform)Audio InferenceModelPipeline) AudioFile) Clustering)PretrainedSpeakerEmbedding) PipelineModelSpeakerDiarizationMixin get_model)binarize batch_sizecCst|g|}tj|d|iS)zBatchify iterable fillvalue)iter itertools zip_longest)iterablerrargsri/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_diarization.pybatchify3sr!csDeZdZdZ         d4d ed ed ed ed ededede e de e dfffdd Z edefddZejdefddZddZddZeddZd5defdd Z  d6d!ed"ed#e efd$d%Zd&ed'ejd(edefd)d*Z     d7d+ed,e ed-e ed.e ed/ed#e edefd0d1Zdefd2d3ZZ S)8SpeakerDiarizationan Speaker diarization pipeline Parameters ---------- segmentation : Model, str, or dict, optional Pretrained segmentation model. Defaults to "pyannote/segmentation@2022.07". See pyannote.audio.pipelines.utils.get_model for supported format. segmentation_step: float, optional The segmentation model is applied on a window sliding over the whole audio file. `segmentation_step` controls the step of this window, provided as a ratio of its duration. Defaults to 0.1 (i.e. 90% overlap between two consecuive windows). embedding : Model, str, or dict, optional Pretrained embedding model. Defaults to "pyannote/embedding@2022.07". See pyannote.audio.pipelines.utils.get_model for supported format. embedding_exclude_overlap : bool, optional Exclude overlapping speech regions when extracting embeddings. Defaults (False) to use the whole speech. clustering : str, optional Clustering algorithm. See pyannote.audio.pipelines.clustering.Clustering for available options. Defaults to "AgglomerativeClustering". segmentation_batch_size : int, optional Batch size used for speaker segmentation. Defaults to 1. embedding_batch_size : int, optional Batch size used for speaker embedding. Defaults to 1. 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` Usage ----- # perform (unconstrained) diarization >>> diarization = pipeline("/path/to/audio.wav") # perform diarization, targetting exactly 4 speakers >>> diarization = pipeline("/path/to/audio.wav", num_speakers=4) # perform diarization, with at least 2 speakers and at most 10 speakers >>> diarization = pipeline("/path/to/audio.wav", min_speakers=2, max_speakers=10) # perform diarization and get one representative embedding per speaker >>> diarization, embeddings = pipeline("/path/to/audio.wav", return_embeddings=True) >>> for s, speaker in enumerate(diarization.labels()): ... # embeddings[s] is the embedding of speaker `speaker` Hyper-parameters ---------------- segmentation.threshold segmentation.min_duration_off clustering.??? pyannote/segmentation@2022.07皙?Jspeechbrain/spkrec-ecapa-voxceleb@5c0be3875fda05e81f3c004ed8c7c06be308de1eFAgglomerativeClusteringN segmentationsegmentation_step embeddingembedding_exclude_overlap clusteringembedding_batch_sizesegmentation_batch_size der_variantuse_auth_tokenc  s*t||_t|| d} ||_||_||_||_||_|p#ddd|_ | j j } t | | |j| d|d|_ |j jj jrFttddd|_n ttd d tddd |_|jd kr[d } nt|j| d|_t|jjdd|_|jj} zt|} Wntytddttjdw| j| d|_ dS)N)r0F)collar skip_overlapT)durationstepskip_aggregationr?)min_duration_offr$g?) thresholdr8OracleClusteringnot_applicabledownmix) sample_ratemonozclustering must be one of [, ])metric)!super__init__segmentation_modelrr)r*r-r+ klusteringr/specificationsr4r _segmentationmodelpowersetr r r(r _embeddingr r=_audiorArKeyError ValueErrorjoinlist __members__valuer,)selfr(r)r*r+r,r-r.r/r0rHsegmentation_durationrA Klustering __class__rr rCssP       zSpeakerDiarization.__init__returncCs|jjSNrGrrRrrr r.sz*SpeakerDiarization.segmentation_batch_sizercCs ||j_dSrXrY)rRrrrr r.s cCstrX)NotImplementedErrorrZrrr default_parameterssz%SpeakerDiarization.default_parametersccs d} d|dV|d7}q)NrTSPEAKER_02dr'r)rRspeakerrrr classess zSpeakerDiarization.classescCsdS)Nztraining_cache/segmentationrrZrrr CACHED_SEGMENTATIONsz&SpeakerDiarization.CACHED_SEGMENTATIONcCsb|dur t|dd}|jr(|j|vr||j}|S|j||d}|||j<|S|j||d}|S)zApply segmentation model Parameter --------- file : AudioFile hook : Optional[Callable] Returns ------- segmentations : (num_chunks, num_frames, num_speakers) SlidingWindowFeature Nr(hook) functoolspartialtrainingrarG)rRfilerc segmentationsrrr get_segmentationss    z$SpeakerDiarization.get_segmentationsbinary_segmentationsexclude_overlaprccsjr dt}d|vr jjjjs|djjkr |dSj j j j \}}}|rWj j} j j} t|| | dtjj ddddk} tj | j n dtj j fd d } t| jd d } t||j}g}|d ur|dd |ddt| dD]1\}}ttdd|\}}t|}t|}j ||d}|||d ur|d|||dqt|}t|d|d}jrjjjjrd|id<|Sjj|dd<|S)aExtract embeddings for each (chunk, speaker) pair Parameters ---------- file : AudioFile binary_segmentations : (num_chunks, num_frames, num_speakers) SlidingWindowFeature Binarized segmentation. exclude_overlap : bool, optional Exclude overlapping speech regions when extracting embeddings. In case non-overlapping speech is too short, use the whole speech. hook: Optional[Callable] Called during embeddings after every batch to report the progress Returns ------- embeddings : (num_chunks, num_speakers, dimension) array ztraining_cache/embeddings embeddingssegmentation.thresholdr7T)axiskeepdimsc3stD]N\\}}\}}jj|dd\}}tj|ddtj}tj|ddtj}t|j|jD]\}}t|krE|}n|}|dt |dfVq7qdS)Npad)r4moder1)nan) ziprKcropnp nan_to_numastypefloat32Tsumtorch from_numpy)chunkmasks_ clean_maskswaveformmask clean_mask used_maskrjclean_segmentationsr4rgmin_num_framesrRrr iter_waveform_and_mask's&  zASpeakerDiarization.get_embeddings..iter_waveform_and_mask)NN)rrNr)total completedr'cSs |dduS)Nrr)brrr Ts z3SpeakerDiarization.get_embeddings..)rz(c s) d -> c s d)c)rmrl)rfgetdictrGrHrFrIr(r9sliding_windowr4datashaperJmin_num_samplesr=mathceilrwr|rr!r- enumeraterufilterr}vstackappendr)rRrgrjrkrccache num_chunks num_frames num_speakersr num_samples clean_framesrbatches batch_countembedding_batchesibatch waveformsrwaveform_batch mask_batchembedding_batchrlrrr get_embeddingssp         z!SpeakerDiarization.get_embeddingsrh hard_clusterscountcCs|jj\}}}t|d}tjt|||f}tt||D]+\} \} \} } t| D]} | dkr4q-tj| dd| | kfdd|| dd| f<q-q t ||j }| ||S)a;Build final discrete diarization out of clustered segmentation Parameters ---------- segmentations : (num_chunks, num_frames, num_speakers) SlidingWindowFeature Raw speaker segmentation. hard_clusters : (num_chunks, num_speakers) array Output of clustering step. count : (total_num_frames, 1) SlidingWindowFeature Instantaneous number of active speakers. Returns ------- discrete_diarization : SlidingWindowFeature Discrete (0s and 1s) diarization. r'Nro) rrrwmaxrtzerosrruuniquerrto_diarization)rRrhrrrrlocal_num_speakers num_clustersclustered_segmentationsrclusterrr(krrr reconstructys&  zSpeakerDiarization.reconstructrgr min_speakers max_speakersreturn_embeddingsc s|j||d}|j|||d\}}}|j||d}|d||jj\}} } |jjjjr.|} n t ||j j dd} |j | |jjj dd} |d| t| jd kret|d d } |rc| td |jjffS| S|sz|d krztj|| ftjd}d}d}n+|jdkr|sd}n|j|| |j|d}|d||j|| |||||jjj d\}}}t|d}||ks||krttd|d|d|d|d t| j|tj| _tj | jddd k}d||<|!||| }|d||j"|d |j j#d} |d | _$d|vr |dr |j%|d| dd \}fd!d"| &Dnd#d"t'| &|(D| j)d$} |s9| S|durB| dfSt*| &|jd krat+|d t*| &|jd fd%f}d&d",D|fd'd(| &D}| |fS))aApply speaker diarization Parameters ---------- file : AudioFile Processed file. num_speakers : int, optional Number of speakers, when known. min_speakers : int, optional Minimum number of speakers. Has no effect when `num_speakers` is provided. max_speakers : int, optional Maximum number of speakers. Has no effect when `num_speakers` is provided. return_embeddings : bool, optional Return representative speaker embeddings. 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 embeddings : np.array, optional Representative speaker embeddings such that `embeddings[i]` is the speaker embedding for i-th speaker in diarization.labels(). Only returned when `return_embeddings` is True. rb)rrrr(F)onset initial_state)r1r1)warm_upspeaker_countingr1uri)rrrn)dtypeNr:)rkrcrl)rlrhr min_clusters max_clustersrgframesr'z2 The detected number of speakers (z/) is outside the given bounds [r?zS]. This can happen if the given audio file is too short to contain zh or more speakers. Try to lower the desired minimal number of speakers. rrdiscrete_diarization)min_duration_onr8 annotationT)return_mappingcsi|] }|||qSr)r).0keymappingrr ^sz,SpeakerDiarization.apply..cSsi|]\}}||qSrr)rlabelexpected_labelrrr rcsr)rrcSsi|]\}}||qSrr)rindexrrrr rscsg|]}|qSrr)rr)inverse_mappingrr sz,SpeakerDiarization.apply..)- setup_hookset_num_speakersrirrrGrHrFrIrr(r9 speaker_countreceptive_fieldrwnanmaxrrrJ dimensionint8rErr+r,rwarningswarntextwrapdedentminimumryr|r to_annotationr8roptimal_mappinglabelsrur` rename_labelslenrritems)rRrgrrrrrcrhrrrbinarized_segmentationsr diarizationrrl centroidsrnum_different_speakersinactive_speakersrr)rrr applys+              zSpeakerDiarization.applycCstdi|jS)Nr)r r/rZrrr get_metricszSpeakerDiarization.get_metric) r#r$r%Fr&r'r'NNrX)FN)NNNFN)!__name__ __module__ __qualname____doc__rfloatboolstrintrrrrrCpropertyr.setterr\r`rarrirrrwndarrayrrrrr r __classcell__rrrUr r":s:  A   5 ^r")rN),rrdrrrrtypingrrrrnumpyrwr}einopsr pyannote.corerrpyannote.metrics.diarizationr pyannote.pipeline.parameterr r pyannote.audior r rrpyannote.audio.core.ior#pyannote.audio.pipelines.clusteringr-pyannote.audio.pipelines.speaker_verificationrpyannote.audio.pipelines.utilsrrrpyannote.audio.utils.signalrrr!r"rrrr s*