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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
from sklearn.cluster import MiniBatchKMeans
import traceback
from multiprocessing import cpu_count
# ###########如果是原始特征要先写save
n_cpu = 0
if n_cpu == 0:
n_cpu = cpu_count()
inp_root = r"./logs/anz/3_feature768"
npys = []
listdir_res = list(os.listdir(inp_root))
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (inp_root, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
print(big_npy.shape) # (6196072, 192)#fp32#4.43G
if big_npy.shape[0] > 2e5:
# if(1):
info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
print(info)
try:
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info = traceback.format_exc()
print(info)
np.save("tools/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")
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf) # mi
print("training")
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
index.train(big_npy)
faiss.write_index(
index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
)
print("adding")
batch_size_add = 8192
for i in range(0, big_npy.shape[0], batch_size_add):
index.add(big_npy[i : i + batch_size_add])
faiss.write_index(
index, "tools/infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf)
)
"""
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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