30 lines
901 B
Python
30 lines
901 B
Python
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]
|