43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""
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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
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"""
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import os
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import logging
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logger = logging.getLogger(__name__)
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import faiss
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import numpy as np
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# ###########如果是原始特征要先写save
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inp_root = r"E:\codes\py39\dataset\mi\2-co256"
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npys = []
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for name in sorted(list(os.listdir(inp_root))):
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phone = np.load("%s/%s" % (inp_root, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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logger.debug(big_npy.shape) # (6196072, 192)#fp32#4.43G
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np.save("infer/big_src_feature_mi.npy", big_npy)
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##################train+add
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
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logger.debug(big_npy.shape)
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index = faiss.index_factory(256, "IVF512,Flat") # mi
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logger.info("Training...")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 9
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index.train(big_npy)
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faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
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logger.info("Adding...")
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index.add(big_npy)
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faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
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"""
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大小（都是FP32）
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big_src_feature 2.95G
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(3098036, 256)
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big_emb 4.43G
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(6196072, 192)
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big_emb双倍是因为求特征要repeat后再加pitch
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"""
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