o ei8@sdZddlZddlmZddlmZzddlmZWney7dZ e d7Z e d7Z e d 7Z e d 7Z ee wddl Z ee Z d d Z d dZddZ   dddZddZdS)zE Utilities for training kmeans model. Author * Pooneh Mousavi 2023 N)tqdm) get_logger)MiniBatchKMeansz=The optional dependency sklearn is needed to use this module z=Cannot import sklearn.cluster.MiniBatchKMeans to use KMeans/ z%Please follow the instructions below z============================= zpip install -U scikit-learn cCs\||}|j\}}||||}}||||jdd}||ddS)aExtract features (output of SSL model) and acculamte them on cpu to be used for clustering. Arguments --------- batch : tensor Single batch of data. features_list : list accumulate features list. ssl_model : torch.nn.Module SSL-model used to extract features used for clustering. ssl_layer_num : int specify output of which layer of the ssl_model should be used. device : str `cpu` or `cuda` device. )end_dimcpuN)tosigflattenextenddetachnumpy)batch features_list ssl_model ssl_layer_numdevicewavswav_lensfeatsrV/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/utils/kmeans.pyaccumulate_and_extract_featuress  rc CsZtj| rtd| dt| Std| dt|||||||||dddd S) a*Return a k-means clustering model with specified parameters. Arguments --------- n_clusters : MiniBatchKMeans The number of clusters to form as well as the number of centroids to generate. init : int Method for initialization: {'k-means++'', ''random''} max_iter : int Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. batch_size : int Size of the mini batches. tol : float Control early stopping based on the relative center changes as measured by a smoothed, variance-normalized of the mean center squared position changes. max_no_improvement :int Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia. n_init : int Number of random initializations that are tried reassignment_ratio : float Control the fraction of the maximum number of counts for a center to be reassigned. random_state :int Determines random number generation for centroid initialization and random reassignment. checkpoint_path : str Path to saved model. Returns ------- MiniBatchKMeans a k-means clustering model with specified parameters. zThe checkpoint is loaded from .zNo checkpoint is found at z(. New model is initialized for training.TN) n_clustersinitmax_iter batch_sizetolmax_no_improvementn_initreassignment_ratio random_stateverbosecompute_labels init_size)ospathexistsloggerinfojoblibloadr) rrrrrr r!r"r#checkpoint_pathrrrfetch_kmeans_model8s( *  r/cCsFtdt||D]}||||}t||krdS||}qdS)a Process data in chunks of a specified size. Arguments --------- data : list The list of integers to be processed. chunk_size : int The size of each chunk. model : MiniBatchKMeans The initial kmeans model for training. rN)rangelen partial_fit)data chunk_sizemodelichunkrrrprocess_chunksys    r8r c Cstdg}d} t|ddk} | D]K} t| ||||t||kr.t|||| d7} g}| d|dkr]td| d|tj |dtj |d |j d |d } t || qt||krrt|||Wd d SWd d S1s}wYd S) aTrain a Kmeans model . Arguments --------- model : MiniBatchKMeans The initial kmeans model for training. train_set : Dataloader Batches of tarining data. ssl_model : torch.nn.Module SSL-model used to extract features used for clustering. save_path: string Path to save intra-checkpoints and dataloader. ssl_layer_num : int Specify output of which layer of the ssl_model should be used. kmeans_batch_size : int Size of the mini batches. device : str `cpu` or `cuda` device. checkpoint_interval: int Determine at which iterations to save the checkpoints. zStart training kmeans model.rT) dynamic_ncolsrz'Saving intra-checkpoints for iteration rzdataloader-TRAIN.ckptzkmeans-cluster-z-layer-z.ptN) r*r+rrr1r8_speechbrain_saver'r(joinr save_model) r5 train_setr save_pathrkmeans_batch_sizercheckpoint_intervalr iterationtrr.rrrtrainsD        "rEcCst|t|ddS)zSave a Kmeans model . Arguments --------- model : MiniBatchKMeans The kmeans model to be saved. checkpoint_path : str Path to save the model. wbN)r,dumpopen)r5r.rrrr>s r>)r9rr:)__doc__r' tqdm.contribrspeechbrain.utils.loggerrsklearn.clusterr ImportErrorerr_msgr,__name__r*rr/r8rEr>rrrrs0   A D