mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-04 19:58:58 +08:00
793 lines
29 KiB
Python
793 lines
29 KiB
Python
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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from infer.modules.vc.modules import VC
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from i18n.i18n import I18nAuto
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from configs.config 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 fairseq
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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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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), 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 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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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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"A4",
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"P4",
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"A50",
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"500",
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"A60",
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"70",
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"80",
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"90",
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"M4",
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"T4",
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"TITAN",
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"4060",
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"L",
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"6000",
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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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gpu_infos.append("%s\t%s" % (i, gpu_name))
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mem.append(
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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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/ 1024
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/ 1024
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+ 0.4
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)
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)
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if if_gpu_ok and len(gpu_infos) > 0:
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gpu_info = "\n".join(gpu_infos)
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default_batch_size = min(mem) // 2
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else:
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
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default_batch_size = 1
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gpus = "-".join([i[0] for i in gpu_infos])
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class ToolButton(gr.Button, gr.components.FormComponent):
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"""Small button with single emoji as text, fits inside gradio forms"""
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def __init__(self, **kwargs):
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super().__init__(variant="tool", **kwargs)
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def get_block_name(self):
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return "button"
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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index_root = os.getenv("index_root")
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outside_index_root = os.getenv("outside_index_root")
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names = []
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths = []
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def lookup_indices(index_root):
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global index_paths
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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lookup_indices(index_root)
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lookup_indices(outside_index_root)
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth") or "onnx" in name:
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uvr5_names.append(name.replace(".pth", ""))
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def change_choices():
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names = []
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths = []
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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return {"choices": sorted(names), "__type__": "update"}, {
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"choices": sorted(index_paths),
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"__type__": "update",
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}
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def clean():
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return {"value": "", "__type__": "update"}
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def export_onnx(ModelPath, ExportedPath):
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from infer.modules.onnx.export import export_onnx as eo
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eo(ModelPath, ExportedPath)
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sr_dict = {
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"32k": 32000,
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"40k": 40000,
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"48k": 48000,
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}
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def if_done(done, p):
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while 1:
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if p.poll() is None:
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sleep(0.5)
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else:
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break
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done[0] = True
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def if_done_multi(done, ps):
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while 1:
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# poll==None代表进程未结束
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# 只要有一个进程未结束都不停
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flag = 1
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for p in ps:
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if p.poll() is None:
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flag = 0
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sleep(0.5)
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break
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if flag == 1:
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break
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done[0] = True
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def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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sr = sr_dict[sr]
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os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
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f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
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f.close()
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cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
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config.python_cmd,
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trainset_dir,
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sr,
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n_p,
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now_dir,
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exp_dir,
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config.noparallel,
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config.preprocess_per,
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)
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logger.info("Execute: " + cmd)
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# , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
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p = Popen(cmd, shell=True)
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# 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
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done = [False]
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threading.Thread(
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target=if_done,
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args=(
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done,
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p,
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),
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).start()
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while 1:
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
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yield (f.read())
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sleep(1)
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if done[0]:
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break
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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logger.info(log)
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yield log
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# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
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def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe):
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gpus = gpus.split("-")
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os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
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f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
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f.close()
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if if_f0:
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if f0method != "rmvpe_gpu":
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cmd = (
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'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
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% (
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config.python_cmd,
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now_dir,
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exp_dir,
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n_p,
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f0method,
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)
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)
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logger.info("Execute: " + cmd)
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p = Popen(
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cmd, shell=True, cwd=now_dir
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) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
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# 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
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done = [False]
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threading.Thread(
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target=if_done,
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args=(
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done,
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p,
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),
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).start()
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else:
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if gpus_rmvpe != "-":
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gpus_rmvpe = gpus_rmvpe.split("-")
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leng = len(gpus_rmvpe)
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ps = []
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for idx, n_g in enumerate(gpus_rmvpe):
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cmd = (
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'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
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% (
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config.python_cmd,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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config.is_half,
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)
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)
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logger.info("Execute: " + cmd)
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p = Popen(
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cmd, shell=True, cwd=now_dir
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) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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# 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
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done = [False]
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threading.Thread(
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target=if_done_multi, #
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args=(
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done,
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ps,
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),
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).start()
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else:
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cmd = (
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config.python_cmd
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+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
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% (
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now_dir,
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exp_dir,
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)
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)
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logger.info("Execute: " + cmd)
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p = Popen(
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cmd, shell=True, cwd=now_dir
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) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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p.wait()
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done = [True]
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while 1:
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with open(
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"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
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) as f:
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yield (f.read())
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sleep(1)
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if done[0]:
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break
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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logger.info(log)
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yield log
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# 对不同part分别开多进程
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"""
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n_part=int(sys.argv[1])
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i_part=int(sys.argv[2])
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i_gpu=sys.argv[3]
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exp_dir=sys.argv[4]
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os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
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"""
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leng = len(gpus)
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ps = []
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for idx, n_g in enumerate(gpus):
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cmd = (
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'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s'
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% (
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config.python_cmd,
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config.device,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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version19,
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config.is_half,
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)
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)
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logger.info("Execute: " + cmd)
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p = Popen(
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cmd, shell=True, cwd=now_dir
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) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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# 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
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done = [False]
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threading.Thread(
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target=if_done_multi,
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args=(
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done,
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ps,
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),
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).start()
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while 1:
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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yield (f.read())
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sleep(1)
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if done[0]:
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break
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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logger.info(log)
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yield log
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def get_pretrained_models(path_str, f0_str, sr2):
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if_pretrained_generator_exist = os.access(
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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logger.warning(
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"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
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path_str,
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f0_str,
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sr2,
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)
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if not if_pretrained_discriminator_exist:
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logger.warning(
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"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
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path_str,
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f0_str,
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sr2,
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)
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return (
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(
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_generator_exist
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else ""
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),
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(
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else ""
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),
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)
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def change_sr2(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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f0_str = "f0" if if_f0_3 else ""
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return get_pretrained_models(path_str, f0_str, sr2)
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def change_version19(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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if sr2 == "32k" and version19 == "v1":
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sr2 = "40k"
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to_return_sr2 = (
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{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
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if version19 == "v1"
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else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
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)
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f0_str = "f0" if if_f0_3 else ""
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return (
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*get_pretrained_models(path_str, f0_str, sr2),
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to_return_sr2,
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)
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# f0method8,pretrained_G14,pretrained_D15
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def change_f0(if_f0_3, sr2, version19):
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path_str = "" if version19 == "v1" else "_v2"
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return (
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{"visible": if_f0_3, "__type__": "update"},
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{"visible": if_f0_3, "__type__": "update"},
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*get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2),
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)
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# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
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def change_info_(ckpt_path):
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if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
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return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
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try:
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with open(
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ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
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) as f:
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info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
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sr, f0 = info["sample_rate"], info["if_f0"]
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version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
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return sr, str(f0), version
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except:
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traceback.print_exc()
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return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
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F0GPUVisible = config.dml == False
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def change_f0_method(f0method8):
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if f0method8 == "rmvpe_gpu":
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visible = F0GPUVisible
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else:
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visible = False
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return {"visible": visible, "__type__": "update"}
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with gr.Blocks(title="RVC WebUI") as app:
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gr.Markdown("## RVC WebUI")
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gr.Markdown(
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value=i18n(
|
||
"本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>."
|
||
)
|
||
)
|
||
with gr.Tabs():
|
||
with gr.TabItem(i18n("模型推理")):
|
||
with gr.Row():
|
||
sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
|
||
with gr.Column():
|
||
refresh_button = gr.Button(
|
||
i18n("刷新音色列表和索引路径"), variant="primary"
|
||
)
|
||
clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
|
||
spk_item = gr.Slider(
|
||
minimum=0,
|
||
maximum=2333,
|
||
step=1,
|
||
label=i18n("请选择说话人id"),
|
||
value=0,
|
||
visible=False,
|
||
interactive=True,
|
||
)
|
||
clean_button.click(
|
||
fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
|
||
)
|
||
with gr.TabItem(i18n("单次推理")):
|
||
with gr.Group():
|
||
with gr.Row():
|
||
with gr.Column():
|
||
vc_transform0 = gr.Number(
|
||
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
|
||
value=0,
|
||
)
|
||
input_audio0 = gr.Textbox(
|
||
label=i18n(
|
||
"输入待处理音频文件路径(默认是正确格式示例)"
|
||
),
|
||
placeholder="C:\\Users\\Desktop\\audio_example.wav",
|
||
)
|
||
file_index1 = gr.Textbox(
|
||
label=i18n(
|
||
"特征检索库文件路径,为空则使用下拉的选择结果"
|
||
),
|
||
placeholder="C:\\Users\\Desktop\\model_example.index",
|
||
interactive=True,
|
||
)
|
||
file_index2 = gr.Dropdown(
|
||
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
|
||
choices=sorted(index_paths),
|
||
interactive=True,
|
||
)
|
||
f0method0 = gr.Radio(
|
||
label=i18n(
|
||
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
|
||
),
|
||
choices=(
|
||
["pm", "harvest", "crepe", "rmvpe"]
|
||
if config.dml == False
|
||
else ["pm", "harvest", "rmvpe"]
|
||
),
|
||
value="rmvpe",
|
||
interactive=True,
|
||
)
|
||
|
||
with gr.Column():
|
||
resample_sr0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=48000,
|
||
label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
|
||
value=0,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
rms_mix_rate0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n(
|
||
"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
|
||
),
|
||
value=0.25,
|
||
interactive=True,
|
||
)
|
||
protect0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=0.5,
|
||
label=i18n(
|
||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||
),
|
||
value=0.33,
|
||
step=0.01,
|
||
interactive=True,
|
||
)
|
||
filter_radius0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=7,
|
||
label=i18n(
|
||
">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"
|
||
),
|
||
value=3,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
index_rate1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=0.75,
|
||
interactive=True,
|
||
)
|
||
f0_file = gr.File(
|
||
label=i18n(
|
||
"F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"
|
||
),
|
||
visible=False,
|
||
)
|
||
|
||
refresh_button.click(
|
||
fn=change_choices,
|
||
inputs=[],
|
||
outputs=[sid0, file_index2],
|
||
api_name="infer_refresh",
|
||
)
|
||
# file_big_npy1 = gr.Textbox(
|
||
# label=i18n("特征文件路径"),
|
||
# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
|
||
# interactive=True,
|
||
# )
|
||
with gr.Group():
|
||
with gr.Column():
|
||
but0 = gr.Button(i18n("转换"), variant="primary")
|
||
with gr.Row():
|
||
vc_output1 = gr.Textbox(label=i18n("输出信息"))
|
||
vc_output2 = gr.Audio(
|
||
label=i18n("输出音频(右下角三个点,点了可以下载)")
|
||
)
|
||
|
||
but0.click(
|
||
vc.vc_single,
|
||
[
|
||
spk_item,
|
||
input_audio0,
|
||
vc_transform0,
|
||
f0_file,
|
||
f0method0,
|
||
file_index1,
|
||
file_index2,
|
||
# file_big_npy1,
|
||
index_rate1,
|
||
filter_radius0,
|
||
resample_sr0,
|
||
rms_mix_rate0,
|
||
protect0,
|
||
],
|
||
[vc_output1, vc_output2],
|
||
api_name="infer_convert",
|
||
)
|
||
with gr.TabItem(i18n("批量推理")):
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
with gr.Column():
|
||
vc_transform1 = gr.Number(
|
||
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
|
||
value=0,
|
||
)
|
||
opt_input = gr.Textbox(
|
||
label=i18n("指定输出文件夹"), value="opt"
|
||
)
|
||
file_index3 = gr.Textbox(
|
||
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
|
||
value="",
|
||
interactive=True,
|
||
)
|
||
file_index4 = gr.Dropdown(
|
||
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
|
||
choices=sorted(index_paths),
|
||
interactive=True,
|
||
)
|
||
f0method1 = gr.Radio(
|
||
label=i18n(
|
||
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
|
||
),
|
||
choices=(
|
||
["pm", "harvest", "crepe", "rmvpe"]
|
||
if config.dml == False
|
||
else ["pm", "harvest", "rmvpe"]
|
||
),
|
||
value="rmvpe",
|
||
interactive=True,
|
||
)
|
||
format1 = gr.Radio(
|
||
label=i18n("导出文件格式"),
|
||
choices=["wav", "flac", "mp3", "m4a"],
|
||
value="wav",
|
||
interactive=True,
|
||
)
|
||
|
||
refresh_button.click(
|
||
fn=lambda: change_choices()[1],
|
||
inputs=[],
|
||
outputs=file_index4,
|
||
api_name="infer_refresh_batch",
|
||
)
|
||
|
||
with gr.Column():
|
||
resample_sr1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=48000,
|
||
label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
|
||
value=0,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
rms_mix_rate1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n(
|
||
"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
|
||
),
|
||
value=1,
|
||
interactive=True,
|
||
)
|
||
protect1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=0.5,
|
||
label=i18n(
|
||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||
),
|
||
value=0.33,
|
||
step=0.01,
|
||
interactive=True,
|
||
)
|
||
filter_radius1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=7,
|
||
label=i18n(
|
||
">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"
|
||
),
|
||
value=3,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
index_rate2 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=1,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
dir_input = gr.Textbox(
|
||
label=i18n(
|
||
"输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"
|
||
),
|
||
placeholder="C:\\Users\\Desktop\\input_vocal_dir",
|
||
)
|
||
inputs = gr.File(
|
||
file_count="multiple",
|
||
label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
|
||
)
|
||
|
||
with gr.Row():
|
||
but1 = gr.Button(i18n("转换"), variant="primary")
|
||
vc_output3 = gr.Textbox(label=i18n("输出信息"))
|
||
|
||
but1.click(
|
||
vc.vc_multi,
|
||
[
|
||
spk_item,
|
||
dir_input,
|
||
opt_input,
|
||
inputs,
|
||
vc_transform1,
|
||
f0method1,
|
||
file_index3,
|
||
file_index4,
|
||
# file_big_npy2,
|
||
index_rate2,
|
||
filter_radius1,
|
||
resample_sr1,
|
||
rms_mix_rate1,
|
||
protect1,
|
||
format1,
|
||
],
|
||
[vc_output3],
|
||
api_name="infer_convert_batch",
|
||
)
|
||
sid0.change(
|
||
fn=vc.get_vc,
|
||
inputs=[sid0, protect0, protect1],
|
||
outputs=[spk_item, protect0, protect1, file_index2, file_index4],
|
||
api_name="infer_change_voice",
|
||
)
|
||
if config.iscolab:
|
||
app.queue(concurrency_count=511, max_size=1022).launch(share=True)
|
||
else:
|
||
app.queue(concurrency_count=511, max_size=1022).launch(
|
||
server_name="localhost",
|
||
inbrowser=not config.noautoopen,
|
||
server_port=config.listen_port,
|
||
quiet=True,
|
||
)
|