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| import os | |
| from glob import glob | |
| from pathlib import Path | |
| import torch | |
| import logging | |
| import argparse | |
| import torch | |
| import numpy as np | |
| from sklearn.cluster import KMeans, MiniBatchKMeans | |
| import tqdm | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| import time | |
| import random | |
| def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False): | |
| logger.info(f"Loading features from {in_dir}") | |
| features = [] | |
| nums = 0 | |
| for path in tqdm.tqdm(in_dir.glob("*.soft.pt")): | |
| features.append(torch.load(path).squeeze(0).numpy().T) | |
| # print(features[-1].shape) | |
| features = np.concatenate(features, axis=0) | |
| print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype) | |
| features = features.astype(np.float32) | |
| logger.info(f"Clustering features of shape: {features.shape}") | |
| t = time.time() | |
| if use_minibatch: | |
| kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features) | |
| else: | |
| kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features) | |
| print(time.time()-t, "s") | |
| x = { | |
| "n_features_in_": kmeans.n_features_in_, | |
| "_n_threads": kmeans._n_threads, | |
| "cluster_centers_": kmeans.cluster_centers_, | |
| } | |
| print("end") | |
| return x | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--dataset', type=Path, default="./dataset/44k", | |
| help='path of training data directory') | |
| parser.add_argument('--output', type=Path, default="logs/44k", | |
| help='path of model output directory') | |
| args = parser.parse_args() | |
| checkpoint_dir = args.output | |
| dataset = args.dataset | |
| n_clusters = 10000 | |
| ckpt = {} | |
| for spk in os.listdir(dataset): | |
| if os.path.isdir(dataset/spk): | |
| print(f"train kmeans for {spk}...") | |
| in_dir = dataset/spk | |
| x = train_cluster(in_dir, n_clusters, verbose=False) | |
| ckpt[spk] = x | |
| checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt" | |
| checkpoint_path.parent.mkdir(exist_ok=True, parents=True) | |
| torch.save( | |
| ckpt, | |
| checkpoint_path, | |
| ) | |
| # import cluster | |
| # for spk in tqdm.tqdm(os.listdir("dataset")): | |
| # if os.path.isdir(f"dataset/{spk}"): | |
| # print(f"start kmeans inference for {spk}...") | |
| # for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)): | |
| # mel_path = feature_path.replace(".discrete.npy",".mel.npy") | |
| # mel_spectrogram = np.load(mel_path) | |
| # feature_len = mel_spectrogram.shape[-1] | |
| # c = np.load(feature_path) | |
| # c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy() | |
| # feature = c.T | |
| # feature_class = cluster.get_cluster_result(feature, spk) | |
| # np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class) | |