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import os, sys |
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import datetime, subprocess |
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from mega import Mega |
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now_dir = os.getcwd() |
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sys.path.append(now_dir) |
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import logging |
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import shutil |
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import threading |
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import traceback |
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import warnings |
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from random import shuffle |
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from subprocess import Popen |
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from time import sleep |
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import json |
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import pathlib |
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|
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import fairseq |
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import faiss |
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import gradio as gr |
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import numpy as np |
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import torch |
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from dotenv import load_dotenv |
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from sklearn.cluster import MiniBatchKMeans |
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|
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from configs.config import Config |
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from i18n.i18n import I18nAuto |
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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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show_info, |
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) |
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from infer.modules.uvr5.modules import uvr |
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from infer.modules.vc.modules import VC |
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logging.getLogger("numba").setLevel(logging.WARNING) |
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|
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logger = logging.getLogger(__name__) |
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|
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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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|
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load_dotenv() |
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config = Config() |
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vc = VC(config) |
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|
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if config.dml == True: |
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|
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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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|
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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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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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|
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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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] |
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): |
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|
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if_gpu_ok = True |
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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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|
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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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|
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def __init__(self, **kwargs): |
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super().__init__(variant="tool", **kwargs) |
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|
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def get_block_name(self): |
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return "button" |
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|
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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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|
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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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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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|
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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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audio_files=[] |
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for filename in os.listdir("./audios"): |
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if filename.endswith(('.wav','.mp3','.ogg')): |
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audio_files.append('./audios/'+filename) |
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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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}, {"choices": sorted(audio_files), "__type__": "update"} |
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|
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def clean(): |
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return {"value": "", "__type__": "update"} |
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|
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def export_onnx(): |
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from infer.modules.onnx.export import export_onnx as eo |
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|
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eo() |
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|
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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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|
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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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|
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def if_done_multi(done, ps): |
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while 1: |
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|
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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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|
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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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per = 3.0 if config.is_half else 3.7 |
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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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per, |
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) |
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logger.info(cmd) |
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p = Popen(cmd, shell=True) |
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|
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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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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(cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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|
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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(cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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ps.append(p) |
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|
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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(cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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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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|
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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' |
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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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) |
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) |
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logger.info(cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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ps.append(p) |
|
|
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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, |
|
ps, |
|
), |
|
).start() |
|
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) |
|
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.warn( |
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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.warn( |
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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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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2) |
|
if if_pretrained_generator_exist |
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else "", |
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) |
|
if if_pretrained_discriminator_exist |
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else "", |
|
) |
|
|
|
|
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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): |
|
path_str = "" if version19 == "v1" else "_v2" |
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if sr2 == "32k" and version19 == "v1": |
|
sr2 = "40k" |
|
to_return_sr2 = ( |
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{"choices": ["40k", "48k"], "__type__": "update", "value": sr2} |
|
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), |
|
to_return_sr2, |
|
) |
|
|
|
|
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def change_f0(if_f0_3, sr2, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
|
return ( |
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{"visible": if_f0_3, "__type__": "update"}, |
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*get_pretrained_models(path_str, "f0", sr2), |
|
) |
|
|
|
|
|
|
|
def click_train( |
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exp_dir1, |
|
sr2, |
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if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
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): |
|
|
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) |
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os.makedirs(exp_dir, exist_ok=True) |
|
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir) |
|
feature_dir = ( |
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"%s/3_feature256" % (exp_dir) |
|
if version19 == "v1" |
|
else "%s/3_feature768" % (exp_dir) |
|
) |
|
if if_f0_3: |
|
f0_dir = "%s/2a_f0" % (exp_dir) |
|
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir) |
|
names = ( |
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set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(feature_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) |
|
) |
|
else: |
|
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( |
|
[name.split(".")[0] for name in os.listdir(feature_dir)] |
|
) |
|
opt = [] |
|
for name in names: |
|
if if_f0_3: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
f0_dir.replace("\\", "\\\\"), |
|
name, |
|
f0nsf_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
else: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
fea_dim = 256 if version19 == "v1" else 768 |
|
if if_f0_3: |
|
for _ in range(2): |
|
opt.append( |
|
"%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" |
|
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) |
|
) |
|
else: |
|
for _ in range(2): |
|
opt.append( |
|
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" |
|
% (now_dir, sr2, now_dir, fea_dim, spk_id5) |
|
) |
|
shuffle(opt) |
|
with open("%s/filelist.txt" % exp_dir, "w") as f: |
|
f.write("\n".join(opt)) |
|
logger.debug("Write filelist done") |
|
|
|
|
|
logger.info("Use gpus: %s", str(gpus16)) |
|
if pretrained_G14 == "": |
|
logger.info("No pretrained Generator") |
|
if pretrained_D15 == "": |
|
logger.info("No pretrained Discriminator") |
|
if version19 == "v1" or sr2 == "40k": |
|
config_path = "v1/%s.json" % sr2 |
|
else: |
|
config_path = "v2/%s.json" % sr2 |
|
config_save_path = os.path.join(exp_dir, "config.json") |
|
if not pathlib.Path(config_save_path).exists(): |
|
with open(config_save_path, "w", encoding="utf-8") as f: |
|
json.dump( |
|
config.json_config[config_path], |
|
f, |
|
ensure_ascii=False, |
|
indent=4, |
|
sort_keys=True, |
|
) |
|
f.write("\n") |
|
if gpus16: |
|
cmd = ( |
|
'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' |
|
% ( |
|
config.python_cmd, |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
gpus16, |
|
total_epoch11, |
|
save_epoch10, |
|
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "", |
|
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "", |
|
1 if if_save_latest13 == i18n("是") else 0, |
|
1 if if_cache_gpu17 == i18n("是") else 0, |
|
1 if if_save_every_weights18 == i18n("是") else 0, |
|
version19, |
|
) |
|
) |
|
else: |
|
cmd = ( |
|
'"%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' |
|
% ( |
|
config.python_cmd, |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
total_epoch11, |
|
save_epoch10, |
|
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "", |
|
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "", |
|
1 if if_save_latest13 == i18n("是") else 0, |
|
1 if if_cache_gpu17 == i18n("是") else 0, |
|
1 if if_save_every_weights18 == i18n("是") else 0, |
|
version19, |
|
) |
|
) |
|
logger.info(cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
p.wait() |
|
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" |
|
|
|
|
|
|
|
def train_index(exp_dir1, version19): |
|
|
|
exp_dir = "logs/%s" % (exp_dir1) |
|
os.makedirs(exp_dir, exist_ok=True) |
|
feature_dir = ( |
|
"%s/3_feature256" % (exp_dir) |
|
if version19 == "v1" |
|
else "%s/3_feature768" % (exp_dir) |
|
) |
|
if not os.path.exists(feature_dir): |
|
return "请先进行特征提取!" |
|
listdir_res = list(os.listdir(feature_dir)) |
|
if len(listdir_res) == 0: |
|
return "请先进行特征提取！" |
|
infos = [] |
|
npys = [] |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, 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] |
|
if big_npy.shape[0] > 2e5: |
|
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) |
|
yield "\n".join(infos) |
|
try: |
|
big_npy = ( |
|
MiniBatchKMeans( |
|
n_clusters=10000, |
|
verbose=True, |
|
batch_size=256 * config.n_cpu, |
|
compute_labels=False, |
|
init="random", |
|
) |
|
.fit(big_npy) |
|
.cluster_centers_ |
|
) |
|
except: |
|
info = traceback.format_exc() |
|
logger.info(info) |
|
infos.append(info) |
|
yield "\n".join(infos) |
|
|
|
np.save("%s/total_fea.npy" % exp_dir, big_npy) |
|
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) |
|
infos.append("%s,%s" % (big_npy.shape, n_ivf)) |
|
yield "\n".join(infos) |
|
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) |
|
|
|
infos.append("training") |
|
yield "\n".join(infos) |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = 1 |
|
index.train(big_npy) |
|
faiss.write_index( |
|
index, |
|
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
|
|
infos.append("adding") |
|
yield "\n".join(infos) |
|
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, |
|
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
infos.append( |
|
"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (n_ivf, index_ivf.nprobe, exp_dir1, version19) |
|
) |
|
|
|
|
|
yield "\n".join(infos) |
|
|
|
|
|
|
|
def train1key( |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
trainset_dir4, |
|
spk_id5, |
|
np7, |
|
f0method8, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
gpus_rmvpe, |
|
): |
|
infos = [] |
|
|
|
def get_info_str(strr): |
|
infos.append(strr) |
|
return "\n".join(infos) |
|
|
|
|
|
yield get_info_str(i18n("step1:正在处理数据")) |
|
[get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)] |
|
|
|
|
|
yield get_info_str(i18n("step2:正在提取音高&正在提取特征")) |
|
[ |
|
get_info_str(_) |
|
for _ in extract_f0_feature( |
|
gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe |
|
) |
|
] |
|
|
|
|
|
yield get_info_str(i18n("step3a:正在训练模型")) |
|
click_train( |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
) |
|
yield get_info_str(i18n("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")) |
|
|
|
|
|
[get_info_str(_) for _ in train_index(exp_dir1, version19)] |
|
yield get_info_str(i18n("全流程结束！")) |
|
|
|
|
|
|
|
def change_info_(ckpt_path): |
|
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")): |
|
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} |
|
try: |
|
with open( |
|
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" |
|
) as f: |
|
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) |
|
sr, f0 = info["sample_rate"], info["if_f0"] |
|
version = "v2" if ("version" in info and info["version"] == "v2") else "v1" |
|
return sr, str(f0), version |
|
except: |
|
traceback.print_exc() |
|
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} |
|
|
|
|
|
F0GPUVisible = config.dml == False |
|
|
|
|
|
def change_f0_method(f0method8): |
|
if f0method8 == "rmvpe_gpu": |
|
visible = F0GPUVisible |
|
else: |
|
visible = False |
|
return {"visible": visible, "__type__": "update"} |
|
|
|
def find_model(): |
|
if len(names) > 0: |
|
vc.get_vc(sorted(names)[0],None,None) |
|
return sorted(names)[0] |
|
else: |
|
try: |
|
gr.Info("Do not forget to choose a model.") |
|
except: |
|
pass |
|
return '' |
|
|
|
def find_audios(index=False): |
|
audio_files=[] |
|
if not os.path.exists('./audios'): os.mkdir("./audios") |
|
for filename in os.listdir("./audios"): |
|
if filename.endswith(('.wav','.mp3','.ogg')): |
|
audio_files.append("./audios/"+filename) |
|
if index: |
|
if len(audio_files) > 0: return sorted(audio_files)[0] |
|
else: return "" |
|
elif len(audio_files) > 0: return sorted(audio_files) |
|
else: return [] |
|
|
|
def get_index(): |
|
if find_model() != '': |
|
chosen_model=sorted(names)[0].split(".")[0] |
|
logs_path="./logs/"+chosen_model |
|
if os.path.exists(logs_path): |
|
for file in os.listdir(logs_path): |
|
if file.endswith(".index"): |
|
return os.path.join(logs_path, file) |
|
return '' |
|
else: |
|
return '' |
|
|
|
def get_indexes(): |
|
indexes_list=[] |
|
for dirpath, dirnames, filenames in os.walk("./logs/"): |
|
for filename in filenames: |
|
if filename.endswith(".index"): |
|
indexes_list.append(os.path.join(dirpath,filename)) |
|
if len(indexes_list) > 0: |
|
return indexes_list |
|
else: |
|
return '' |
|
|
|
def save_wav(file): |
|
try: |
|
file_path=file.name |
|
shutil.move(file_path,'./audios') |
|
return './audios/'+os.path.basename(file_path) |
|
except AttributeError: |
|
try: |
|
new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' |
|
new_path='./audios/'+new_name |
|
shutil.move(file,new_path) |
|
return new_path |
|
except TypeError: |
|
return None |
|
|
|
def download_from_url(url, model): |
|
if url == '': |
|
return "URL cannot be left empty." |
|
if model =='': |
|
return "You need to name your model. For example: My-Model" |
|
url = url.strip() |
|
zip_dirs = ["zips", "unzips"] |
|
for directory in zip_dirs: |
|
if os.path.exists(directory): |
|
shutil.rmtree(directory) |
|
os.makedirs("zips", exist_ok=True) |
|
os.makedirs("unzips", exist_ok=True) |
|
zipfile = model + '.zip' |
|
zipfile_path = './zips/' + zipfile |
|
try: |
|
if "drive.google.com" in url: |
|
subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) |
|
elif "mega.nz" in url: |
|
m = Mega() |
|
m.download_url(url, './zips') |
|
else: |
|
subprocess.run(["wget", url, "-O", zipfile_path]) |
|
for filename in os.listdir("./zips"): |
|
if filename.endswith(".zip"): |
|
zipfile_path = os.path.join("./zips/",filename) |
|
shutil.unpack_archive(zipfile_path, "./unzips", 'zip') |
|
else: |
|
return "No zipfile found." |
|
for root, dirs, files in os.walk('./unzips'): |
|
for file in files: |
|
file_path = os.path.join(root, file) |
|
if file.endswith(".index"): |
|
os.mkdir(f'./logs/{model}') |
|
shutil.copy2(file_path,f'./logs/{model}') |
|
elif "G_" not in file and "D_" not in file and file.endswith(".pth"): |
|
shutil.copy(file_path,f'./assets/weights/{model}.pth') |
|
shutil.rmtree("zips") |
|
shutil.rmtree("unzips") |
|
return "Success." |
|
except: |
|
return "There's been an error." |
|
|
|
def upload_to_dataset(files, dir): |
|
if dir == '': |
|
dir = './dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") |
|
if not os.path.exists(dir): |
|
os.makedirs(dir) |
|
for file in files: |
|
path=file.name |
|
shutil.copy2(path,dir) |
|
try: |
|
gr.Info(i18n("处理数据")) |
|
except: |
|
pass |
|
return i18n("处理数据"), {"value":dir,"__type__":"update"} |
|
|
|
def download_model_files(model): |
|
model_found = False |
|
index_found = False |
|
if os.path.exists(f'./assets/weights/{model}.pth'): model_found = True |
|
if os.path.exists(f'./logs/{model}'): |
|
for file in os.listdir(f'./logs/{model}'): |
|
if file.endswith('.index') and 'added' in file: |
|
log_file = file |
|
index_found = True |
|
if model_found and index_found: |
|
return [f'./assets/weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" |
|
elif model_found and not index_found: |
|
return f'./assets/weights/{model}.pth', "Could not find Index file." |
|
elif index_found and not model_found: |
|
return f'./logs/{model}/{log_file}', f'Make sure the Voice Name is correct. I could not find {model}.pth' |
|
else: |
|
return None, f'Could not find {model}.pth or corresponding Index file.' |
|
|
|
with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="rose",neutral_hue="zinc")) as app: |
|
with gr.Row(): |
|
gr.HTML("<img src='file/a.png' alt='image'>") |
|
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), value=find_model()) |
|
refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), 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, |
|
) |
|
|
|
|
|
|
|
vc_transform0 = gr.Number( |
|
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 |
|
) |
|
but0 = gr.Button(i18n("转换"), variant="primary") |
|
with gr.Row(): |
|
with gr.Column(): |
|
with gr.Row(): |
|
dropbox = gr.File(label="Drop your audio here & hit the Reload button.") |
|
with gr.Row(): |
|
record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") |
|
with gr.Row(): |
|
input_audio0 = gr.Dropdown( |
|
label=i18n("输入待处理音频文件路径(默认是正确格式示例)"), |
|
value=find_audios(True), |
|
choices=find_audios() |
|
) |
|
record_button.change(fn=save_wav, inputs=[record_button], outputs=[input_audio0]) |
|
dropbox.upload(fn=save_wav, inputs=[dropbox], outputs=[input_audio0]) |
|
with gr.Column(): |
|
with gr.Accordion(label=i18n("自动检测index路径,下拉式选择(dropdown)"), open=False): |
|
file_index2 = gr.Dropdown( |
|
label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
|
choices=get_indexes(), |
|
interactive=True, |
|
value=get_index() |
|
) |
|
index_rate1 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("检索特征占比"), |
|
value=0.66, |
|
interactive=True, |
|
) |
|
vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) |
|
with gr.Accordion(label=i18n("常规设置"), open=False): |
|
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, |
|
) |
|
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, |
|
) |
|
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, |
|
visible=False |
|
) |
|
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=0.21, |
|
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, |
|
) |
|
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"), |
|
value="", |
|
interactive=True, |
|
visible=False |
|
) |
|
refresh_button.click( |
|
fn=change_choices, |
|
inputs=[], |
|
outputs=[sid0, file_index2, input_audio0], |
|
api_name="infer_refresh", |
|
) |
|
|
|
|
|
|
|
|
|
|
|
with gr.Row(): |
|
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) |
|
with gr.Row(): |
|
vc_output1 = gr.Textbox(label=i18n("输出信息")) |
|
but0.click( |
|
vc.vc_single, |
|
[ |
|
spk_item, |
|
input_audio0, |
|
vc_transform0, |
|
f0_file, |
|
f0method0, |
|
file_index1, |
|
file_index2, |
|
|
|
index_rate1, |
|
filter_radius0, |
|
resample_sr0, |
|
rms_mix_rate0, |
|
protect0, |
|
], |
|
[vc_output1, vc_output2], |
|
api_name="infer_convert", |
|
) |
|
with gr.Row(): |
|
with gr.Accordion(open=False, label=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(): |
|
opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") |
|
vc_transform1 = gr.Number( |
|
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 |
|
) |
|
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="pm", |
|
interactive=True, |
|
) |
|
with gr.Row(): |
|
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, |
|
visible=False |
|
) |
|
with gr.Row(): |
|
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, |
|
visible=False |
|
) |
|
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, |
|
visible=False |
|
) |
|
refresh_button.click( |
|
fn=lambda: change_choices()[1], |
|
inputs=[], |
|
outputs=file_index4, |
|
api_name="infer_refresh_batch", |
|
) |
|
|
|
|
|
|
|
|
|
|
|
index_rate2 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("检索特征占比"), |
|
value=1, |
|
interactive=True, |
|
visible=False |
|
) |
|
with gr.Row(): |
|
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, |
|
visible=False |
|
) |
|
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=0.21, |
|
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, |
|
) |
|
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)"), |
|
value="./audios", |
|
) |
|
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(): |
|
format1 = gr.Radio( |
|
label=i18n("导出文件格式"), |
|
choices=["wav", "flac", "mp3", "m4a"], |
|
value="wav", |
|
interactive=True, |
|
) |
|
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_index1, |
|
file_index2, |
|
|
|
index_rate1, |
|
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], |
|
) |
|
with gr.TabItem("Download Model"): |
|
with gr.Row(): |
|
url=gr.Textbox(label="Enter the URL to the Model:") |
|
with gr.Row(): |
|
model = gr.Textbox(label="Name your model:") |
|
download_button=gr.Button("Download") |
|
with gr.Row(): |
|
status_bar=gr.Textbox(label="") |
|
download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) |
|
with gr.Row(): |
|
gr.Markdown( |
|
""" |
|
❤️ If you use this and like it, help me keep it.❤️ |
|
https://paypal.me/lesantillan |
|
""" |
|
) |
|
with gr.TabItem(i18n("训练")): |
|
with gr.Row(): |
|
with gr.Column(): |
|
exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="My-Voice") |
|
np7 = gr.Slider( |
|
minimum=0, |
|
maximum=config.n_cpu, |
|
step=1, |
|
label=i18n("提取音高和处理数据使用的CPU进程数"), |
|
value=int(np.ceil(config.n_cpu / 1.5)), |
|
interactive=True, |
|
) |
|
sr2 = gr.Radio( |
|
label=i18n("目标采样率"), |
|
choices=["40k", "48k"], |
|
value="40k", |
|
interactive=True, |
|
visible=False |
|
) |
|
if_f0_3 = gr.Radio( |
|
label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), |
|
choices=[True, False], |
|
value=True, |
|
interactive=True, |
|
visible=False |
|
) |
|
version19 = gr.Radio( |
|
label=i18n("版本"), |
|
choices=["v1", "v2"], |
|
value="v2", |
|
interactive=True, |
|
visible=False, |
|
) |
|
trainset_dir4 = gr.Textbox( |
|
label=i18n("输入训练文件夹路径"), value='./dataset/'+datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") |
|
) |
|
easy_uploader = gr.Files(label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),file_types=['audio']) |
|
but1 = gr.Button(i18n("处理数据"), variant="primary") |
|
info1 = gr.Textbox(label=i18n("输出信息"), value="") |
|
easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1, trainset_dir4]) |
|
gpus6 = gr.Textbox( |
|
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
|
value=gpus, |
|
interactive=True, |
|
visible=F0GPUVisible, |
|
) |
|
gpu_info9 = gr.Textbox( |
|
label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible |
|
) |
|
spk_id5 = gr.Slider( |
|
minimum=0, |
|
maximum=4, |
|
step=1, |
|
label=i18n("请指定说话人id"), |
|
value=0, |
|
interactive=True, |
|
visible=False |
|
) |
|
but1.click( |
|
preprocess_dataset, |
|
[trainset_dir4, exp_dir1, sr2, np7], |
|
[info1], |
|
api_name="train_preprocess", |
|
) |
|
with gr.Column(): |
|
f0method8 = gr.Radio( |
|
label=i18n( |
|
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU" |
|
), |
|
choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
|
value="rmvpe_gpu", |
|
interactive=True, |
|
) |
|
gpus_rmvpe = gr.Textbox( |
|
label=i18n( |
|
"rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" |
|
), |
|
value="%s-%s" % (gpus, gpus), |
|
interactive=True, |
|
visible=F0GPUVisible, |
|
) |
|
but2 = gr.Button(i18n("特征提取"), variant="primary") |
|
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) |
|
f0method8.change( |
|
fn=change_f0_method, |
|
inputs=[f0method8], |
|
outputs=[gpus_rmvpe], |
|
) |
|
but2.click( |
|
extract_f0_feature, |
|
[ |
|
gpus6, |
|
np7, |
|
f0method8, |
|
if_f0_3, |
|
exp_dir1, |
|
version19, |
|
gpus_rmvpe, |
|
], |
|
[info2], |
|
api_name="train_extract_f0_feature", |
|
) |
|
with gr.Column(): |
|
total_epoch11 = gr.Slider( |
|
minimum=2, |
|
maximum=1000, |
|
step=1, |
|
label=i18n("总训练轮数total_epoch"), |
|
value=150, |
|
interactive=True, |
|
) |
|
gpus16 = gr.Textbox( |
|
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
|
value="0", |
|
interactive=True, |
|
visible=True |
|
) |
|
but3 = gr.Button(i18n("训练模型"), variant="primary") |
|
but4 = gr.Button(i18n("训练特征索引"), variant="primary") |
|
info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) |
|
with gr.Accordion(label=i18n("常规设置"), open=False): |
|
save_epoch10 = gr.Slider( |
|
minimum=1, |
|
maximum=50, |
|
step=1, |
|
label=i18n("保存频率save_every_epoch"), |
|
value=25, |
|
interactive=True, |
|
) |
|
batch_size12 = gr.Slider( |
|
minimum=1, |
|
maximum=40, |
|
step=1, |
|
label=i18n("每张显卡的batch_size"), |
|
value=default_batch_size, |
|
interactive=True, |
|
) |
|
if_save_latest13 = gr.Radio( |
|
label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), |
|
choices=[i18n("是"), i18n("否")], |
|
value=i18n("是"), |
|
interactive=True, |
|
visible=False |
|
) |
|
if_cache_gpu17 = gr.Radio( |
|
label=i18n( |
|
"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" |
|
), |
|
choices=[i18n("是"), i18n("否")], |
|
value=i18n("否"), |
|
interactive=True, |
|
) |
|
if_save_every_weights18 = gr.Radio( |
|
label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), |
|
choices=[i18n("是"), i18n("否")], |
|
value=i18n("是"), |
|
interactive=True, |
|
) |
|
with gr.Row(): |
|
download_model = gr.Button('5.Download Model') |
|
with gr.Row(): |
|
model_files = gr.Files(label='Your Model and Index file can be downloaded here:') |
|
download_model.click(fn=download_model_files, inputs=[exp_dir1], outputs=[model_files, info3]) |
|
with gr.Row(): |
|
pretrained_G14 = gr.Textbox( |
|
label=i18n("加载预训练底模G路径"), |
|
value="assets/pretrained_v2/f0G40k.pth", |
|
interactive=True, |
|
visible=False |
|
) |
|
pretrained_D15 = gr.Textbox( |
|
label=i18n("加载预训练底模D路径"), |
|
value="assets/pretrained_v2/f0D40k.pth", |
|
interactive=True, |
|
visible=False |
|
) |
|
sr2.change( |
|
change_sr2, |
|
[sr2, if_f0_3, version19], |
|
[pretrained_G14, pretrained_D15], |
|
) |
|
version19.change( |
|
change_version19, |
|
[sr2, if_f0_3, version19], |
|
[pretrained_G14, pretrained_D15, sr2], |
|
) |
|
if_f0_3.change( |
|
change_f0, |
|
[if_f0_3, sr2, version19], |
|
[f0method8, pretrained_G14, pretrained_D15], |
|
) |
|
with gr.Row(): |
|
but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) |
|
but3.click( |
|
click_train, |
|
[ |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
], |
|
info3, |
|
api_name="train_start", |
|
) |
|
but4.click(train_index, [exp_dir1, version19], info3) |
|
but5.click( |
|
train1key, |
|
[ |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
trainset_dir4, |
|
spk_id5, |
|
np7, |
|
f0method8, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
gpus_rmvpe, |
|
], |
|
info3, |
|
api_name="train_start_all", |
|
) |
|
|
|
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="0.0.0.0", |
|
inbrowser=not config.noautoopen, |
|
server_port=config.listen_port, |
|
quiet=True, |
|
) |
|
|
