| import numpy as np |
| import torch |
| from sklearn.cluster import KMeans |
|
|
| def get_cluster_model(ckpt_path): |
| checkpoint = torch.load(ckpt_path) |
| kmeans_dict = {} |
| for spk, ckpt in checkpoint.items(): |
| km = KMeans(ckpt["n_features_in_"]) |
| km.__dict__["n_features_in_"] = ckpt["n_features_in_"] |
| km.__dict__["_n_threads"] = ckpt["_n_threads"] |
| km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"] |
| kmeans_dict[spk] = km |
| return kmeans_dict |
|
|
| def get_cluster_result(model, x, speaker): |
| """ |
| x: np.array [t, 256] |
| return cluster class result |
| """ |
| return model[speaker].predict(x) |
|
|
| def get_cluster_center_result(model, x,speaker): |
| """x: np.array [t, 256]""" |
| predict = model[speaker].predict(x) |
| return model[speaker].cluster_centers_[predict] |
|
|
| def get_center(model, x,speaker): |
| return model[speaker].cluster_centers_[x] |
|
|
