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from sklearn.cluster import *
import os
import numpy as np
from config import config
import yaml
from multiprocessing import Pool
from tqdm import tqdm
def process_speaker(speaker):
embs = []
wavnames = []
print("\nspeaker: " + speaker)
for file in filelist_dict[speaker]:
try:
embs.append(
np.expand_dims(np.load(f"{os.path.splitext(file)[0]}.emo.npy"), axis=0)
)
wavnames.append(os.path.basename(file))
except Exception as e:
print(e)
x = np.concatenate(embs, axis=0)
x = np.squeeze(x)
# 聚类算法类的数量
n_clusters = config.emo_cluster_config.n_clusters
method = config.emo_cluster_config.method
if method == "b":
model = Birch(n_clusters=n_clusters, threshold=0.2)
elif method == "s":
model = SpectralClustering(n_clusters=n_clusters)
elif method == "a":
model = AgglomerativeClustering(n_clusters=n_clusters)
else:
model = KMeans(n_clusters=n_clusters, random_state=42)
# 可以自行尝试各种不同的聚类算法
y_predict = model.fit_predict(x)
classes = [[] for i in range(y_predict.max() + 1)]
for idx, wavname in enumerate(wavnames):
classes[y_predict[idx]].append(wavname)
yml_result = {}
yml_result[speaker] = {}
os.makedirs(
os.path.join(config.dataset_path, f"emo_clustering/{speaker}"), exist_ok=True
)
for i in range(y_predict.max() + 1):
class_length = len(classes[i])
print("类别:", i, "本类中样本数量:", class_length)
yml_result[speaker][f"class{i}"] = []
for j in range(config.emo_cluster_config.n_samples):
if j >= class_length:
break
print(classes[i][j])
yml_result[speaker][f"class{i}"].append(classes[i][j])
if hasattr(model, "cluster_centers_") and config.emo_cluster_config.save_center:
centers = model.cluster_centers_
filename = os.path.join(
config.dataset_path, f"emo_clustering/{speaker}/cluster_center_{i}.npy"
)
# 保存中心
np.save(filename, centers[i])
elif config.emo_cluster_config.save_center:
labels = model.labels_
centers = np.array([X[labels == i].mean(0) for i in range(n_clusters)])
filename = os.path.join(
config.dataset_path, f"emo_clustering/{speaker}/cluster_center_{i}.npy"
)
np.save(filename, centers[i])
return yml_result
if __name__ == "__main__":
filelist_dict = {}
with open(
config.preprocess_text_config.train_path, mode="r", encoding="utf-8"
) as f:
for line in f:
speaker = line.split("|")[1]
if speaker not in filelist_dict:
filelist_dict[speaker] = []
filelist_dict[speaker].append(line.split("|")[0])
with Pool() as p:
results = list(
tqdm(
p.imap(process_speaker, list(filelist_dict.keys())),
total=len(filelist_dict),
)
)
yml_result = {}
for result in results:
yml_result.update(result)
with open(
os.path.join(config.dataset_path, "emo_clustering/emo_clustering.yml"),
"w",
encoding="utf-8",
) as f:
yaml.dump(yml_result, f)
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