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File size: 1,269 Bytes
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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 os
import logging
logger = logging.getLogger(__name__)
import faiss
import numpy as np
# ###########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)
logger.debug(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")
logger.debug(big_npy.shape)
index = faiss.index_factory(256, "IVF512,Flat") # mi
logger.info("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")
logger.info("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
"""
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