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from sklearn.cluster import *
from sklearn import metrics
from sklearn.mixture import GaussianMixture # 高斯混合模型
import os
import numpy as np
import config#
import yaml
#import argparse
if __name__ == "__main__":
#parser = argparse.ArgumentParser()
config.parser.add_argument("-a","--algorithm", default="k",help="choose algorithm",type=str)
config.parser.add_argument("-n","--num_clusters", default=3,help="number of clusters",type=int)
config.parser.add_argument("-r","--range", default=4,help="number of files in a class",type=int)
args = config.parser.parse_args()
filelist_dict={}
yml_result={}
from config import config
with open(config.preprocess_text_config.cleaned_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]=[]
yml_result[speaker]={}
filelist_dict[speaker].append(line.split("|")[0])
#print(filelist_dict)
for speaker in filelist_dict:
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 = args.num_clusters
if args.algorithm=="b":
model = Birch(n_clusters= n_clusters, threshold= 0.2)
elif args.algorithm=="s":
model = SpectralClustering(n_clusters=n_clusters)
elif args.algorithm=="a":
model = AgglomerativeClustering(n_clusters= n_clusters)
else:
model = KMeans(n_clusters=n_clusters, random_state=10)
# 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)
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(args.range):
if j >=class_length:
break
print(classes[i][j])
yml_result[speaker][f"class{i}"].append(classes[i][j])
with open(os.path.join(config.dataset_path,'emo_clustering.yml'), 'w', encoding='utf-8') as f:
yaml.dump(yml_result, f) |