imLdZddlmZddlZddlmZddlmZddl m Z ddl m Z ddl mZdd lmZdd lmZmZdd lmZdd lmZmZmZdd lmZmZddlmZddlm Z ddl!m"Z"GddeZ#Gdde#Z$Gdde#Z%Gdde#Z&Gdde#Z'GddeZ(y)zClustering pipelines)EnumN) rearrange) AudioFile)PLDA)oracle_segmentation) permutate) cluster_vbx) SlidingWindowSlidingWindowFeature)Pipeline) CategoricalIntegerUniform)fclusterlinkage)linear_sum_assignment)cdist)KMeansc eZdZ ddedeffd Z ddededzdedzdedzfd Z dd ejd e dzd e d e ejejejffdZ dejd ejfdZ dd ejdejdejdejdef dZ dd ejd e dzdedzdedzdedzd ejf dZxZS)BaseClusteringmetricconstrained_assignmentc>t|||_||_y)N)super__init__rrselfrr __class__s x/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/pyannote/audio/pipelines/clustering.pyrzBaseClustering.__init__-s  &<#Nnum_embeddings num_clusters min_clusters max_clustersc|xs|xsd}tdt||}|xs|xs|}tdt||}||kDrtd|dd|dd||k(r|}|||fS)NzQmin_clusters must be smaller than (or equal to) max_clusters (here: min_clusters=gz and max_clusters=z).)maxmin ValueError)rr!r"r#r$s rset_num_clusterszBaseClustering.set_num_clusters6s$8|8q 1c.,?@ #E|E~ 1c.,?@ , &''3A&66HVWHXXZ\  < ''L\<77r embeddings segmentationsmin_active_ratioreturncx|jj\}}}tj|jdddk(}tj|j|zd}|||zk\}tjtj |d} tj || z\} } || | f| | fS)aFilter embeddings before clustering Embeddings that are removed: * NaN embeddings * embeddings speaking less than `min_active_ratio` times the chunk duration Parameters ---------- embeddings : (num_chunks, num_speakers, dimension) array Sequence of embeddings. segmentations : (num_chunks, num_frames, num_speakers) array Binary segmentations. min_active_ratio : float, optional Minimum active ratio for a speaker to be considered active during clustering. Returns ------- filtered_embeddings : (num_embeddings, dimension) array chunk_idx : (num_embeddings, ) array speaker_idx : (num_embeddings, ) array Taxiskeepdimsr&r3)datashapenpsumanyisnanwhere) rr,r-r._ num_framessingle_active_masknum_clean_framesactivevalid chunk_idx speaker_idxs rfilter_embeddingsz BaseClustering.filter_embeddingsMs:)--33:q!ff]%7%7a$OSTT66-"4"47I"IPQR"%5 %BB,155"$&5.!9 ;)[019kIIr soft_clusterscJtj|tj|}|j\}}}dtj||ftj z}t |D]0\}}t|d\}} t|| D] \} } | ||| f<2|S)N)nandtypeT)maximize) r8 nan_to_numnanminr7onesint8 enumeraterzip) rrF num_chunks num_speakersr" hard_clustersccostspeakersclusterssks rconstrained_argmaxz!BaseClustering.constrained_argmaxs m=9QR 1>1D1D. L,RWWj,%?rwwOO  /GAt!6td!K HhHh/1&' ad#00 r train_chunk_idxtrain_speaker_idxtrain_clusters constrainedc tj|dz}|j\}}} |||f} tjt |D cgc]} tj | || k(d!c} } t tt |d| |jd||} d| z }|r|j|}ntj|d}||| fScc} w) aAssign embeddings to the closest centroid Cluster centroids are computed as the average of the train embeddings previously assigned to them. Parameters ---------- embeddings : (num_chunks, num_speakers, dimension)-shaped array Complete set of embeddings. train_chunk_idx : (num_embeddings,)-shaped array train_speaker_idx : (num_embeddings,)-shaped array Indices of subset of embeddings used for "training". train_clusters : (num_embedding,)-shaped array Clusters of the above subset constrained : bool, optional Use constrained_argmax, instead of (default) argmax. Returns ------- soft_clusters : (num_chunks, num_speakers, num_clusters)-shaped array hard_clusters : (num_chunks, num_speakers)-shaped array centroids : (num_clusters, dimension)-shaped array Clusters centroids r&rr5c s d -> (c s) dr(c s) k -> c s krVrZr1) r8r(r7vstackrangemeanrrrr\argmax)rr,r]r^r_r`r"rSrT dimensiontrain_embeddingsr[ centroids e2k_distancerFrUs rassign_embeddingsz BaseClustering.assign_embeddingssFvvn-1 .8.>.>+ L)%o7H&HIII|, ,A(1)<=AF,  ! *&89{{    L(   33MBMIIm! 8Y@?v   s&N1$N>$O1N;rurwryrzr{__doc__rr}__annotations__r|rr8rr~rrrrs@rrr$s""'$&',//!%/*$(#'#' VJJVDjVDj V Dj Vr rc eZdZUdZdZeed< d deffd Z d de jde dzd e dzd e dzfd Z xZ S)KMeansClusteringzKMeans clustering Parameters ---------- metric : {"cosine", "euclidean"}, optional Distance metric to use. Defaults to "cosine". Hyper-parameters ---------------- None TrrcL|dvrtd|dt| |y)N)rvrzUnsupported metric: z". Must be 'cosine' or 'euclidean'.rc)r*rr)rrrs rrzKMeansClustering.__init__s: 0 0&vh.PQ  'r Nr,r#r$r"c| td|j\}}||kr%tj|tjS|j dk(rEtj dd5|tjj|dd z}dddt|d d d j|S#1swY'xYw)aYPerform KMeans clustering Parameters ---------- embeddings : (num_embeddings, dimension) array Embeddings num_clusters : int, optional Expected number of clusters. Returns ------- clusters : (num_embeddings, ) array 0-indexed cluster indices. Nz `num_clusters` must be provided.rJrvrrrTr2r*F n_clustersn_init random_statecopy_x) r*r7r8rint32rrrrr fit_predict)rr,r#r$r"r!r=s rrrzKMeansClustering.clusters,  ?@ @&,, L (99^288< < ;;( "Hh?biinnZb4nPP @#ABu +j ! 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