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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"./logs/nene/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 | |
| 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") | |
| 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, "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, "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 | |
| """ | |