Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,805 @@
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-
import gradio as gr
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from Apllio import *
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1 |
from Apllio import *
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2 |
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import os
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import sys
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from dotenv import load_dotenv
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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load_dotenv()
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load_dotenv("sha256.env")
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if sys.platform == "darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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from infer.modules.vc import VC, show_info, hash_similarity
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from infer.modules.uvr5.modules import uvr
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from infer.lib.train.process_ckpt import (
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change_info,
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extract_small_model,
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merge,
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)
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from i18n.i18n import I18nAuto
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from configs import Config
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from sklearn.cluster import MiniBatchKMeans
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import torch, platform
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import numpy as np
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import gradio as gr
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import faiss
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import pathlib
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import json
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from time import sleep
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from subprocess import Popen
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from random import shuffle
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import warnings
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import traceback
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import threading
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import shutil
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import logging
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("httpx").setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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tmp = os.path.join(now_dir, "TEMP")
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shutil.rmtree(tmp, ignore_errors=True)
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os.makedirs(tmp, exist_ok=True)
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
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os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True)
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os.environ["TEMP"] = tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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config = Config()
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vc = VC(config)
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if not config.nocheck:
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from infer.lib.rvcmd import check_all_assets, download_all_assets
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if not check_all_assets(update=config.update):
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if config.update:
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download_all_assets(tmpdir=tmp)
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if not check_all_assets(update=config.update):
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logging.error("counld not satisfy all assets needed.")
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exit(1)
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if config.dml == True:
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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return res
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import fairseq
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
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i18n = I18nAuto()
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logger.info(i18n)
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# 判断是否有能用来训练和加速推理的N卡
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ngpu = torch.cuda.device_count()
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gpu_infos = []
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mem = []
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if_gpu_ok = False
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if torch.cuda.is_available() or ngpu != 0:
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for i in range(ngpu):
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gpu_name = torch.cuda.get_device_name(i)
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if any(
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value in gpu_name.upper()
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for value in [
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"10",
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"16",
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"20",
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"30",
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"40",
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"A2",
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"A3",
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100 |
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"A4",
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"P4",
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102 |
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"A50",
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103 |
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"500",
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104 |
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"A60",
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"70",
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106 |
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"80",
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107 |
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"90",
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108 |
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"M4",
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109 |
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"T4",
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110 |
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"TITAN",
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111 |
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"4060",
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112 |
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"L",
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113 |
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"6000",
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114 |
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]
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):
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# A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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118 |
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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119 |
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mem.append(
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120 |
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int(
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torch.cuda.get_device_properties(i).total_memory
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/ 1024
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123 |
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/ 1024
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124 |
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/ 1024
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125 |
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+ 0.4
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126 |
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)
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127 |
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)
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128 |
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if if_gpu_ok and len(gpu_infos) > 0:
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129 |
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gpu_info = "\n".join(gpu_infos)
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130 |
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default_batch_size = min(mem) // 2
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131 |
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else:
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132 |
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gpu_info = i18n(
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133 |
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"Unfortunately, there is no compatible GPU available to support your training."
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134 |
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)
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135 |
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default_batch_size = 1
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136 |
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gpus = "-".join([i[0] for i in gpu_infos])
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137 |
+
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138 |
+
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139 |
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weight_root = os.getenv("weight_root")
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140 |
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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141 |
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index_root = os.getenv("index_root")
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142 |
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outside_index_root = os.getenv("outside_index_root")
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143 |
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144 |
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names = []
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145 |
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for name in os.listdir(weight_root):
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146 |
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if name.endswith(".pth"):
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147 |
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names.append(name)
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148 |
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index_paths = []
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149 |
+
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150 |
+
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151 |
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def lookup_indices(index_root):
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152 |
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global index_paths
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153 |
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for root, dirs, files in os.walk(index_root, topdown=False):
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154 |
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for name in files:
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155 |
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if name.endswith(".index") and "trained" not in name:
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156 |
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index_paths.append("%s/%s" % (root, name))
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157 |
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158 |
+
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159 |
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lookup_indices(index_root)
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160 |
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lookup_indices(outside_index_root)
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161 |
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uvr5_names = []
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162 |
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for name in os.listdir(weight_uvr5_root):
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163 |
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if name.endswith(".pth") or "onnx" in name:
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164 |
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uvr5_names.append(name.replace(".pth", ""))
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165 |
+
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166 |
+
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167 |
+
def change_choices():
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168 |
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names = []
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169 |
+
for name in os.listdir(weight_root):
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170 |
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if name.endswith(".pth"):
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171 |
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names.append(name)
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172 |
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index_paths = []
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173 |
+
for root, dirs, files in os.walk(index_root, topdown=False):
|
174 |
+
for name in files:
|
175 |
+
if name.endswith(".index") and "trained" not in name:
|
176 |
+
index_paths.append("%s/%s" % (root, name))
|
177 |
+
return {"choices": sorted(names), "__type__": "update"}, {
|
178 |
+
"choices": sorted(index_paths),
|
179 |
+
"__type__": "update",
|
180 |
+
}
|
181 |
+
|
182 |
+
|
183 |
+
def clean():
|
184 |
+
return {"value": "", "__type__": "update"}
|
185 |
+
|
186 |
+
|
187 |
+
def export_onnx(ModelPath, ExportedPath):
|
188 |
+
from rvc.onnx import export_onnx as eo
|
189 |
+
|
190 |
+
eo(ModelPath, ExportedPath)
|
191 |
+
|
192 |
+
|
193 |
+
sr_dict = {
|
194 |
+
"32k": 32000,
|
195 |
+
"40k": 40000,
|
196 |
+
"48k": 48000,
|
197 |
+
}
|
198 |
+
|
199 |
+
|
200 |
+
def if_done(done, p):
|
201 |
+
while 1:
|
202 |
+
if p.poll() is None:
|
203 |
+
sleep(0.5)
|
204 |
+
else:
|
205 |
+
break
|
206 |
+
done[0] = True
|
207 |
+
|
208 |
+
|
209 |
+
def if_done_multi(done, ps):
|
210 |
+
while 1:
|
211 |
+
# poll==None代表进程未结束
|
212 |
+
# 只要有一个进程未结束都不停
|
213 |
+
flag = 1
|
214 |
+
for p in ps:
|
215 |
+
if p.poll() is None:
|
216 |
+
flag = 0
|
217 |
+
sleep(0.5)
|
218 |
+
break
|
219 |
+
if flag == 1:
|
220 |
+
break
|
221 |
+
done[0] = True
|
222 |
+
|
223 |
+
|
224 |
+
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
|
225 |
+
sr = sr_dict[sr]
|
226 |
+
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
227 |
+
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
|
228 |
+
f.close()
|
229 |
+
cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
|
230 |
+
config.python_cmd,
|
231 |
+
trainset_dir,
|
232 |
+
sr,
|
233 |
+
n_p,
|
234 |
+
now_dir,
|
235 |
+
exp_dir,
|
236 |
+
config.noparallel,
|
237 |
+
config.preprocess_per,
|
238 |
+
)
|
239 |
+
logger.info("Execute: " + cmd)
|
240 |
+
# , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
|
241 |
+
p = Popen(cmd, shell=True)
|
242 |
+
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
243 |
+
done = [False]
|
244 |
+
threading.Thread(
|
245 |
+
target=if_done,
|
246 |
+
args=(
|
247 |
+
done,
|
248 |
+
p,
|
249 |
+
),
|
250 |
+
).start()
|
251 |
+
while 1:
|
252 |
+
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
253 |
+
yield (f.read())
|
254 |
+
sleep(1)
|
255 |
+
if done[0]:
|
256 |
+
break
|
257 |
+
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
|
258 |
+
log = f.read()
|
259 |
+
logger.info(log)
|
260 |
+
yield log
|
261 |
+
|
262 |
+
|
263 |
+
# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
|
264 |
+
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe):
|
265 |
+
gpus = gpus.split("-")
|
266 |
+
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
267 |
+
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
|
268 |
+
f.close()
|
269 |
+
if if_f0:
|
270 |
+
if f0method != "rmvpe_gpu":
|
271 |
+
cmd = (
|
272 |
+
'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
|
273 |
+
% (
|
274 |
+
config.python_cmd,
|
275 |
+
now_dir,
|
276 |
+
exp_dir,
|
277 |
+
n_p,
|
278 |
+
f0method,
|
279 |
+
)
|
280 |
+
)
|
281 |
+
logger.info("Execute: " + cmd)
|
282 |
+
p = Popen(
|
283 |
+
cmd, shell=True, cwd=now_dir
|
284 |
+
) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
|
285 |
+
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
286 |
+
done = [False]
|
287 |
+
threading.Thread(
|
288 |
+
target=if_done,
|
289 |
+
args=(
|
290 |
+
done,
|
291 |
+
p,
|
292 |
+
),
|
293 |
+
).start()
|
294 |
+
else:
|
295 |
+
if gpus_rmvpe != "-":
|
296 |
+
gpus_rmvpe = gpus_rmvpe.split("-")
|
297 |
+
leng = len(gpus_rmvpe)
|
298 |
+
ps = []
|
299 |
+
for idx, n_g in enumerate(gpus_rmvpe):
|
300 |
+
cmd = (
|
301 |
+
'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
|
302 |
+
% (
|
303 |
+
config.python_cmd,
|
304 |
+
leng,
|
305 |
+
idx,
|
306 |
+
n_g,
|
307 |
+
now_dir,
|
308 |
+
exp_dir,
|
309 |
+
config.is_half,
|
310 |
+
)
|
311 |
+
)
|
312 |
+
logger.info("Execute: " + cmd)
|
313 |
+
p = Popen(
|
314 |
+
cmd, shell=True, cwd=now_dir
|
315 |
+
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
316 |
+
ps.append(p)
|
317 |
+
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
318 |
+
done = [False]
|
319 |
+
threading.Thread(
|
320 |
+
target=if_done_multi, #
|
321 |
+
args=(
|
322 |
+
done,
|
323 |
+
ps,
|
324 |
+
),
|
325 |
+
).start()
|
326 |
+
else:
|
327 |
+
cmd = (
|
328 |
+
config.python_cmd
|
329 |
+
+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
|
330 |
+
% (
|
331 |
+
now_dir,
|
332 |
+
exp_dir,
|
333 |
+
)
|
334 |
+
)
|
335 |
+
logger.info("Execute: " + cmd)
|
336 |
+
p = Popen(
|
337 |
+
cmd, shell=True, cwd=now_dir
|
338 |
+
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
339 |
+
p.wait()
|
340 |
+
done = [True]
|
341 |
+
while 1:
|
342 |
+
with open(
|
343 |
+
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
|
344 |
+
) as f:
|
345 |
+
yield (f.read())
|
346 |
+
sleep(1)
|
347 |
+
if done[0]:
|
348 |
+
break
|
349 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
350 |
+
log = f.read()
|
351 |
+
logger.info(log)
|
352 |
+
yield log
|
353 |
+
# 对不同part分别开多进程
|
354 |
+
"""
|
355 |
+
n_part=int(sys.argv[1])
|
356 |
+
i_part=int(sys.argv[2])
|
357 |
+
i_gpu=sys.argv[3]
|
358 |
+
exp_dir=sys.argv[4]
|
359 |
+
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
|
360 |
+
"""
|
361 |
+
leng = len(gpus)
|
362 |
+
ps = []
|
363 |
+
for idx, n_g in enumerate(gpus):
|
364 |
+
cmd = (
|
365 |
+
'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s'
|
366 |
+
% (
|
367 |
+
config.python_cmd,
|
368 |
+
config.device,
|
369 |
+
leng,
|
370 |
+
idx,
|
371 |
+
n_g,
|
372 |
+
now_dir,
|
373 |
+
exp_dir,
|
374 |
+
version19,
|
375 |
+
config.is_half,
|
376 |
+
)
|
377 |
+
)
|
378 |
+
logger.info("Execute: " + cmd)
|
379 |
+
p = Popen(
|
380 |
+
cmd, shell=True, cwd=now_dir
|
381 |
+
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
382 |
+
ps.append(p)
|
383 |
+
# 煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
|
384 |
+
done = [False]
|
385 |
+
threading.Thread(
|
386 |
+
target=if_done_multi,
|
387 |
+
args=(
|
388 |
+
done,
|
389 |
+
ps,
|
390 |
+
),
|
391 |
+
).start()
|
392 |
+
while 1:
|
393 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
394 |
+
yield (f.read())
|
395 |
+
sleep(1)
|
396 |
+
if done[0]:
|
397 |
+
break
|
398 |
+
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
|
399 |
+
log = f.read()
|
400 |
+
logger.info(log)
|
401 |
+
yield log
|
402 |
+
|
403 |
+
|
404 |
+
def get_pretrained_models(path_str, f0_str, sr2):
|
405 |
+
if_pretrained_generator_exist = os.access(
|
406 |
+
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
|
407 |
+
)
|
408 |
+
if_pretrained_discriminator_exist = os.access(
|
409 |
+
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
|
410 |
+
)
|
411 |
+
if not if_pretrained_generator_exist:
|
412 |
+
logger.warning(
|
413 |
+
"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
|
414 |
+
path_str,
|
415 |
+
f0_str,
|
416 |
+
sr2,
|
417 |
+
)
|
418 |
+
if not if_pretrained_discriminator_exist:
|
419 |
+
logger.warning(
|
420 |
+
"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
|
421 |
+
path_str,
|
422 |
+
f0_str,
|
423 |
+
sr2,
|
424 |
+
)
|
425 |
+
return (
|
426 |
+
(
|
427 |
+
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
|
428 |
+
if if_pretrained_generator_exist
|
429 |
+
else ""
|
430 |
+
),
|
431 |
+
(
|
432 |
+
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
|
433 |
+
if if_pretrained_discriminator_exist
|
434 |
+
else ""
|
435 |
+
),
|
436 |
+
)
|
437 |
+
|
438 |
+
|
439 |
+
def change_sr2(sr2, if_f0_3, version19):
|
440 |
+
path_str = "" if version19 == "v1" else "_v2"
|
441 |
+
f0_str = "f0" if if_f0_3 else ""
|
442 |
+
return get_pretrained_models(path_str, f0_str, sr2)
|
443 |
+
|
444 |
+
|
445 |
+
def change_version19(sr2, if_f0_3, version19):
|
446 |
+
path_str = "" if version19 == "v1" else "_v2"
|
447 |
+
if sr2 == "32k" and version19 == "v1":
|
448 |
+
sr2 = "40k"
|
449 |
+
to_return_sr2 = (
|
450 |
+
{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
|
451 |
+
if version19 == "v1"
|
452 |
+
else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
|
453 |
+
)
|
454 |
+
f0_str = "f0" if if_f0_3 else ""
|
455 |
+
return (
|
456 |
+
*get_pretrained_models(path_str, f0_str, sr2),
|
457 |
+
to_return_sr2,
|
458 |
+
)
|
459 |
+
|
460 |
+
|
461 |
+
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
|
462 |
+
path_str = "" if version19 == "v1" else "_v2"
|
463 |
+
return (
|
464 |
+
{"visible": if_f0_3, "__type__": "update"},
|
465 |
+
{"visible": if_f0_3, "__type__": "update"},
|
466 |
+
*get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2),
|
467 |
+
)
|
468 |
+
|
469 |
+
|
470 |
+
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
|
471 |
+
def click_train(
|
472 |
+
exp_dir1,
|
473 |
+
sr2,
|
474 |
+
if_f0_3,
|
475 |
+
spk_id5,
|
476 |
+
save_epoch10,
|
477 |
+
total_epoch11,
|
478 |
+
batch_size12,
|
479 |
+
if_save_latest13,
|
480 |
+
pretrained_G14,
|
481 |
+
pretrained_D15,
|
482 |
+
gpus16,
|
483 |
+
if_cache_gpu17,
|
484 |
+
if_save_every_weights18,
|
485 |
+
version19,
|
486 |
+
author,
|
487 |
+
):
|
488 |
+
# 生成filelist
|
489 |
+
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
490 |
+
os.makedirs(exp_dir, exist_ok=True)
|
491 |
+
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
|
492 |
+
feature_dir = (
|
493 |
+
"%s/3_feature256" % (exp_dir)
|
494 |
+
if version19 == "v1"
|
495 |
+
else "%s/3_feature768" % (exp_dir)
|
496 |
+
)
|
497 |
+
if if_f0_3:
|
498 |
+
f0_dir = "%s/2a_f0" % (exp_dir)
|
499 |
+
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
|
500 |
+
names = (
|
501 |
+
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
502 |
+
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
503 |
+
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
504 |
+
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
505 |
+
)
|
506 |
+
else:
|
507 |
+
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
508 |
+
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
509 |
+
)
|
510 |
+
opt = []
|
511 |
+
for name in names:
|
512 |
+
if if_f0_3:
|
513 |
+
opt.append(
|
514 |
+
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
|
515 |
+
% (
|
516 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
517 |
+
name,
|
518 |
+
feature_dir.replace("\\", "\\\\"),
|
519 |
+
name,
|
520 |
+
f0_dir.replace("\\", "\\\\"),
|
521 |
+
name,
|
522 |
+
f0nsf_dir.replace("\\", "\\\\"),
|
523 |
+
name,
|
524 |
+
spk_id5,
|
525 |
+
)
|
526 |
+
)
|
527 |
+
else:
|
528 |
+
opt.append(
|
529 |
+
"%s/%s.wav|%s/%s.npy|%s"
|
530 |
+
% (
|
531 |
+
gt_wavs_dir.replace("\\", "\\\\"),
|
532 |
+
name,
|
533 |
+
feature_dir.replace("\\", "\\\\"),
|
534 |
+
name,
|
535 |
+
spk_id5,
|
536 |
+
)
|
537 |
+
)
|
538 |
+
fea_dim = 256 if version19 == "v1" else 768
|
539 |
+
if if_f0_3:
|
540 |
+
for _ in range(2):
|
541 |
+
opt.append(
|
542 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
543 |
+
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
|
544 |
+
)
|
545 |
+
else:
|
546 |
+
for _ in range(2):
|
547 |
+
opt.append(
|
548 |
+
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
|
549 |
+
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
|
550 |
+
)
|
551 |
+
shuffle(opt)
|
552 |
+
with open("%s/filelist.txt" % exp_dir, "w") as f:
|
553 |
+
f.write("\n".join(opt))
|
554 |
+
logger.debug("Write filelist done")
|
555 |
+
logger.info("Use gpus: %s", str(gpus16))
|
556 |
+
if pretrained_G14 == "":
|
557 |
+
logger.info("No pretrained Generator")
|
558 |
+
if pretrained_D15 == "":
|
559 |
+
logger.info("No pretrained Discriminator")
|
560 |
+
if version19 == "v1" or sr2 == "40k":
|
561 |
+
config_path = "v1/%s.json" % sr2
|
562 |
+
else:
|
563 |
+
config_path = "v2/%s.json" % sr2
|
564 |
+
config_save_path = os.path.join(exp_dir, "config.json")
|
565 |
+
if not pathlib.Path(config_save_path).exists():
|
566 |
+
with open(config_save_path, "w", encoding="utf-8") as f:
|
567 |
+
json.dump(
|
568 |
+
config.json_config[config_path],
|
569 |
+
f,
|
570 |
+
ensure_ascii=False,
|
571 |
+
indent=4,
|
572 |
+
sort_keys=True,
|
573 |
+
)
|
574 |
+
f.write("\n")
|
575 |
+
cmd = (
|
576 |
+
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -a "%s"'
|
577 |
+
% (
|
578 |
+
config.python_cmd,
|
579 |
+
exp_dir1,
|
580 |
+
sr2,
|
581 |
+
1 if if_f0_3 else 0,
|
582 |
+
batch_size12,
|
583 |
+
total_epoch11,
|
584 |
+
save_epoch10,
|
585 |
+
'-pg "%s"' % pretrained_G14 if pretrained_G14 != "" else "",
|
586 |
+
'-pd "%s"' % pretrained_D15 if pretrained_D15 != "" else "",
|
587 |
+
1 if if_save_latest13 == i18n("Yes") else 0,
|
588 |
+
1 if if_cache_gpu17 == i18n("Yes") else 0,
|
589 |
+
1 if if_save_every_weights18 == i18n("Yes") else 0,
|
590 |
+
version19,
|
591 |
+
author,
|
592 |
+
)
|
593 |
+
)
|
594 |
+
if gpus16:
|
595 |
+
cmd += ' -g "%s"' % (gpus16)
|
596 |
+
|
597 |
+
logger.info("Execute: " + cmd)
|
598 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
599 |
+
p.wait()
|
600 |
+
return "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
|
601 |
+
|
602 |
+
|
603 |
+
# but4.click(train_index, [exp_dir1], info3)
|
604 |
+
def train_index(exp_dir1, version19):
|
605 |
+
# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
606 |
+
exp_dir = "logs/%s" % (exp_dir1)
|
607 |
+
os.makedirs(exp_dir, exist_ok=True)
|
608 |
+
feature_dir = (
|
609 |
+
"%s/3_feature256" % (exp_dir)
|
610 |
+
if version19 == "v1"
|
611 |
+
else "%s/3_feature768" % (exp_dir)
|
612 |
+
)
|
613 |
+
if not os.path.exists(feature_dir):
|
614 |
+
return "请先进行特征提取!"
|
615 |
+
listdir_res = list(os.listdir(feature_dir))
|
616 |
+
if len(listdir_res) == 0:
|
617 |
+
return "请先进行特征提取!"
|
618 |
+
infos = []
|
619 |
+
npys = []
|
620 |
+
for name in sorted(listdir_res):
|
621 |
+
phone = np.load("%s/%s" % (feature_dir, name))
|
622 |
+
npys.append(phone)
|
623 |
+
big_npy = np.concatenate(npys, 0)
|
624 |
+
big_npy_idx = np.arange(big_npy.shape[0])
|
625 |
+
np.random.shuffle(big_npy_idx)
|
626 |
+
big_npy = big_npy[big_npy_idx]
|
627 |
+
if big_npy.shape[0] > 2e5:
|
628 |
+
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
|
629 |
+
yield "\n".join(infos)
|
630 |
+
try:
|
631 |
+
big_npy = (
|
632 |
+
MiniBatchKMeans(
|
633 |
+
n_clusters=10000,
|
634 |
+
verbose=True,
|
635 |
+
batch_size=256 * config.n_cpu,
|
636 |
+
compute_labels=False,
|
637 |
+
init="random",
|
638 |
+
)
|
639 |
+
.fit(big_npy)
|
640 |
+
.cluster_centers_
|
641 |
+
)
|
642 |
+
except:
|
643 |
+
info = traceback.format_exc()
|
644 |
+
logger.info(info)
|
645 |
+
infos.append(info)
|
646 |
+
yield "\n".join(infos)
|
647 |
+
|
648 |
+
np.save("%s/total_fea.npy" % exp_dir, big_npy)
|
649 |
+
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
|
650 |
+
infos.append("%s,%s" % (big_npy.shape, n_ivf))
|
651 |
+
yield "\n".join(infos)
|
652 |
+
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
|
653 |
+
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
|
654 |
+
infos.append("training")
|
655 |
+
yield "\n".join(infos)
|
656 |
+
index_ivf = faiss.extract_index_ivf(index) #
|
657 |
+
index_ivf.nprobe = 1
|
658 |
+
index.train(big_npy)
|
659 |
+
faiss.write_index(
|
660 |
+
index,
|
661 |
+
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
662 |
+
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
663 |
+
)
|
664 |
+
infos.append("adding")
|
665 |
+
yield "\n".join(infos)
|
666 |
+
batch_size_add = 8192
|
667 |
+
for i in range(0, big_npy.shape[0], batch_size_add):
|
668 |
+
index.add(big_npy[i : i + batch_size_add])
|
669 |
+
index_save_path = "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
|
670 |
+
exp_dir,
|
671 |
+
n_ivf,
|
672 |
+
index_ivf.nprobe,
|
673 |
+
exp_dir1,
|
674 |
+
version19,
|
675 |
+
)
|
676 |
+
faiss.write_index(index, index_save_path)
|
677 |
+
infos.append(i18n("Successfully built index into") + " " + index_save_path)
|
678 |
+
link_target = "%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
|
679 |
+
outside_index_root,
|
680 |
+
exp_dir1,
|
681 |
+
n_ivf,
|
682 |
+
index_ivf.nprobe,
|
683 |
+
exp_dir1,
|
684 |
+
version19,
|
685 |
+
)
|
686 |
+
try:
|
687 |
+
link = os.link if platform.system() == "Windows" else os.symlink
|
688 |
+
link(index_save_path, link_target)
|
689 |
+
infos.append(i18n("Link index to outside folder") + " " + link_target)
|
690 |
+
except:
|
691 |
+
infos.append(
|
692 |
+
i18n("Link index to outside folder")
|
693 |
+
+ " "
|
694 |
+
+ link_target
|
695 |
+
+ " "
|
696 |
+
+ i18n("Fail")
|
697 |
+
)
|
698 |
+
|
699 |
+
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
700 |
+
# infos.append("成功构建索引,added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
|
701 |
+
yield "\n".join(infos)
|
702 |
+
|
703 |
+
|
704 |
+
# 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)
|
705 |
+
def train1key(
|
706 |
+
exp_dir1,
|
707 |
+
sr2,
|
708 |
+
if_f0_3,
|
709 |
+
trainset_dir4,
|
710 |
+
spk_id5,
|
711 |
+
np7,
|
712 |
+
f0method8,
|
713 |
+
save_epoch10,
|
714 |
+
total_epoch11,
|
715 |
+
batch_size12,
|
716 |
+
if_save_latest13,
|
717 |
+
pretrained_G14,
|
718 |
+
pretrained_D15,
|
719 |
+
gpus16,
|
720 |
+
if_cache_gpu17,
|
721 |
+
if_save_every_weights18,
|
722 |
+
version19,
|
723 |
+
gpus_rmvpe,
|
724 |
+
author,
|
725 |
+
):
|
726 |
+
infos = []
|
727 |
+
|
728 |
+
def get_info_str(strr):
|
729 |
+
infos.append(strr)
|
730 |
+
return "\n".join(infos)
|
731 |
+
|
732 |
+
# step1:Process data
|
733 |
+
yield get_info_str(i18n("Step 1: Processing data"))
|
734 |
+
[get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
|
735 |
+
|
736 |
+
# step2a:提取音高
|
737 |
+
yield get_info_str(i18n("step2:Pitch extraction & feature extraction"))
|
738 |
+
[
|
739 |
+
get_info_str(_)
|
740 |
+
for _ in extract_f0_feature(
|
741 |
+
gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
|
742 |
+
)
|
743 |
+
]
|
744 |
+
|
745 |
+
# step3a:Train model
|
746 |
+
yield get_info_str(i18n("Step 3a: Model training started"))
|
747 |
+
click_train(
|
748 |
+
exp_dir1,
|
749 |
+
sr2,
|
750 |
+
if_f0_3,
|
751 |
+
spk_id5,
|
752 |
+
save_epoch10,
|
753 |
+
total_epoch11,
|
754 |
+
batch_size12,
|
755 |
+
if_save_latest13,
|
756 |
+
pretrained_G14,
|
757 |
+
pretrained_D15,
|
758 |
+
gpus16,
|
759 |
+
if_cache_gpu17,
|
760 |
+
if_save_every_weights18,
|
761 |
+
version19,
|
762 |
+
author,
|
763 |
+
)
|
764 |
+
yield get_info_str(
|
765 |
+
i18n(
|
766 |
+
"Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
|
767 |
+
)
|
768 |
+
)
|
769 |
+
|
770 |
+
# step3b:训练索引
|
771 |
+
[get_info_str(_) for _ in train_index(exp_dir1, version19)]
|
772 |
+
yield get_info_str(i18n("All processes have been completed!"))
|
773 |
+
|
774 |
+
|
775 |
+
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
776 |
+
def change_info_(ckpt_path):
|
777 |
+
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
|
778 |
+
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
779 |
+
try:
|
780 |
+
with open(
|
781 |
+
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
|
782 |
+
) as f:
|
783 |
+
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
|
784 |
+
sr, f0 = info["sample_rate"], info["if_f0"]
|
785 |
+
version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
|
786 |
+
return sr, str(f0), version
|
787 |
+
except:
|
788 |
+
traceback.print_exc()
|
789 |
+
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
790 |
+
|
791 |
+
|
792 |
+
F0GPUVisible = config.dml == False
|
793 |
+
|
794 |
+
|
795 |
+
def change_f0_method(f0method8):
|
796 |
+
if f0method8 == "rmvpe_gpu":
|
797 |
+
visible = F0GPUVisible
|
798 |
+
else:
|
799 |
+
visible = False
|
800 |
+
return {"visible": visible, "__type__": "update"}
|
801 |
+
|
802 |
+
|
803 |
|
804 |
|
805 |
|