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""" | |
格式:直接cid为自带的index位;aid放不下了,通过字典来查,反正就5w个 | |
""" | |
import faiss, numpy as np, os | |
# ###########如果是原始特征要先写save | |
inp_root = r"E:\codes\py39\dataset\mi\2-co256" | |
npys = [] | |
for name in sorted(list(os.listdir(inp_root))): | |
phone = np.load("%s/%s" % (inp_root, name)) | |
npys.append(phone) | |
big_npy = np.concatenate(npys, 0) | |
print(big_npy.shape) # (6196072, 192)#fp32#4.43G | |
np.save("infer/big_src_feature_mi.npy", big_npy) | |
##################train+add | |
# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy") | |
print(big_npy.shape) | |
index = faiss.index_factory(256, "IVF512,Flat") # mi | |
print("training") | |
index_ivf = faiss.extract_index_ivf(index) # | |
index_ivf.nprobe = 9 | |
index.train(big_npy) | |
faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index") | |
print("adding") | |
index.add(big_npy) | |
faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index") | |
""" | |
大小(都是FP32) | |
big_src_feature 2.95G | |
(3098036, 256) | |
big_emb 4.43G | |
(6196072, 192) | |
big_emb双倍是因为求特征要repeat后再加pitch | |
""" | |