mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-05-06 20:01:37 +08:00
faiss improvement:1 use recommended parameter
tmp
This commit is contained in:
nadare
parent
334da847d2
commit
29c78c6416
72
infer-web.py
72
infer-web.py
@ -72,7 +72,7 @@ from config import (
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noautoopen,
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)
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from infer_uvr5 import _audio_pre_
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from my_utils import load_audio
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from my_utils import load_audio, train_index
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from train.process_ckpt import show_info, change_info, merge, extract_small_model
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# from trainset_preprocess_pipeline import PreProcess
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@ -614,47 +614,6 @@ def click_train(
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return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
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# but4.click(train_index, [exp_dir1], info3)
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def train_index(exp_dir1):
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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feature_dir = "%s/3_feature256" % (exp_dir)
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if os.path.exists(feature_dir) == False:
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return "请先进行特征提取!"
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listdir_res = list(os.listdir(feature_dir))
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if len(listdir_res) == 0:
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return "请先进行特征提取！"
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npys = []
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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n_ivf = big_npy.shape[0] // 39
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infos = []
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infos.append("%s,%s" % (big_npy.shape, n_ivf))
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yield "\n".join(infos)
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index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
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infos.append("training")
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yield "\n".join(infos)
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = int(np.power(n_ivf, 0.3))
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index.train(big_npy)
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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infos.append("adding")
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yield "\n".join(infos)
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index.add(big_npy)
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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infos.append("成功构建索引, added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe))
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yield "\n".join(infos)
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# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
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def train1key(
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exp_dir1,
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@ -835,34 +794,7 @@ def train1key(
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p.wait()
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yield get_info_str("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")
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#######step3b:训练索引
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feature_dir = "%s/3_feature256" % (exp_dir)
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npys = []
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listdir_res = list(os.listdir(feature_dir))
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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n_ivf = big_npy.shape[0] // 39
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yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
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index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
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yield get_info_str("training index")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = int(np.power(n_ivf, 0.3))
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index.train(big_npy)
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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yield get_info_str("adding index")
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index.add(big_npy)
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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yield get_info_str(
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"成功构建索引, added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe)
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)
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yield from train_index(exp_dir1)
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yield get_info_str("全流程结束！")
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64
my_utils.py
64
my_utils.py
@ -1,7 +1,71 @@
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import os
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import faiss
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import ffmpeg
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import numpy as np
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def train_index(exp_dir1):
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'''train and save faiss index
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Args:
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exp_dir1(string): Relative path where index is stored
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'''
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exp_dir = "%s/logs/%s" % (os.getcwd(), exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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feature_dir = "%s/3_feature256" % (exp_dir)
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if os.path.exists(feature_dir) == False:
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return "请先进行特征提取!"
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listdir_res = list(os.listdir(feature_dir))
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if len(listdir_res) == 0:
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return "请先进行特征提取！"
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npys = []
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for name in sorted(listdir_res):
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phone = np.load("%s/%s" % (feature_dir, name))
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npys.append(phone)
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big_npy = np.concatenate(npys, 0)
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np.save("%s/total_fea.npy" % exp_dir, big_npy)
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# use recommended parameter in https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
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N = big_npy.shape[0]
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dim = big_npy.shape[1]
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if 4 * np.rint(np.sqrt(N)) * 30 > N:
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n_ivf = N // 30
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else:
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n_ivf = -(-N // 256)
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for x in range(4, 18, 2):
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K = x * np.rint(np.sqrt(N)).astype(int)
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if 30 * K <= N <= 256 * K:
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n_ivf = K
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break
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index_string = "IVF%s,PQ%sx4fs,RFlat" % (n_ivf, -(-dim//2))
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index_name = index_string.replace(",", "_")
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infos = []
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infos.append("%s,%s" % (big_npy.shape, n_ivf))
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yield "\n".join(infos)
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index = faiss.index_factory(dim, index_string)
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infos.append("training")
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yield "\n".join(infos)
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index.train(big_npy)
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faiss.write_index(
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index,
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"%s/trained_%s.index" % (exp_dir, index_name),
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)
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infos.append("adding")
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yield "\n".join(infos)
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index.add(big_npy)
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faiss.write_index(
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index,
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"%s/added_%s.index" % (exp_dir, index_name),
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)
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infos.append("成功构建索引, added_%s.index" % (index_name))
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yield "\n".join(infos)
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def load_audio(file, sr):
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try:
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# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
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