mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
1520 lines
60 KiB
Python
1520 lines
60 KiB
Python
import os, sys
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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 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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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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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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load_dotenv()
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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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]
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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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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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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():
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from infer.modules.onnx.export import export_onnx as eo
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eo()
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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 get_quoted_python_cmd():
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return f'"{config.python_cmd}"'
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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 = (
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get_quoted_python_cmd()
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+ ' infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" '
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% (trainset_dir, sr, n_p, now_dir, exp_dir)
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+ str(config.noparallel)
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)
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logger.info(cmd)
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p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
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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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get_quoted_python_cmd()
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+ ' infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
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% (
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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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) # , 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 = get_quoted_python_cmd() + ' infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s ' % (
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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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logger.info(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(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 = get_quoted_python_cmd() + ' infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s' % (
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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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logger.info(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 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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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" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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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" % (path_str, f0_str, sr2),
|
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"not exist, will not use pretrained model",
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)
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return (
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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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"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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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"
|
||
to_return_sr2 = (
|
||
{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
|
||
if version19 == "v1"
|
||
else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
|
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)
|
||
f0_str = "f0" if if_f0_3 else ""
|
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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
|
||
)
|
||
if_pretrained_discriminator_exist = os.access(
|
||
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
|
||
)
|
||
if not if_pretrained_generator_exist:
|
||
logger.warn(
|
||
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
|
||
"not exist, will not use pretrained model",
|
||
)
|
||
if not if_pretrained_discriminator_exist:
|
||
logger.warn(
|
||
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
|
||
"not exist, will not use pretrained model",
|
||
)
|
||
return (
|
||
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
|
||
if if_pretrained_generator_exist
|
||
else "",
|
||
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
|
||
if if_pretrained_discriminator_exist
|
||
else "",
|
||
to_return_sr2,
|
||
)
|
||
|
||
|
||
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
|
||
path_str = "" if version19 == "v1" else "_v2"
|
||
if_pretrained_generator_exist = os.access(
|
||
"assets/pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
|
||
)
|
||
if_pretrained_discriminator_exist = os.access(
|
||
"assets/pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
|
||
)
|
||
if not if_pretrained_generator_exist:
|
||
logger.warn(
|
||
"assets/pretrained%s/f0G%s.pth" % (path_str, sr2),
|
||
"not exist, will not use pretrained model",
|
||
)
|
||
if not if_pretrained_discriminator_exist:
|
||
logger.warn(
|
||
"assets/pretrained%s/f0D%s.pth" % (path_str, sr2),
|
||
"not exist, will not use pretrained model",
|
||
)
|
||
if if_f0_3:
|
||
return (
|
||
{"visible": True, "__type__": "update"},
|
||
"assets/pretrained%s/f0G%s.pth" % (path_str, sr2)
|
||
if if_pretrained_generator_exist
|
||
else "",
|
||
"assets/pretrained%s/f0D%s.pth" % (path_str, sr2)
|
||
if if_pretrained_discriminator_exist
|
||
else "",
|
||
)
|
||
return (
|
||
{"visible": False, "__type__": "update"},
|
||
("assets/pretrained%s/G%s.pth" % (path_str, sr2))
|
||
if if_pretrained_generator_exist
|
||
else "",
|
||
("assets/pretrained%s/D%s.pth" % (path_str, sr2))
|
||
if if_pretrained_discriminator_exist
|
||
else "",
|
||
)
|
||
|
||
|
||
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
|
||
def 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,
|
||
):
|
||
# 生成filelist
|
||
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
||
os.makedirs(exp_dir, exist_ok=True)
|
||
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
|
||
feature_dir = (
|
||
"%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 = (
|
||
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")
|
||
# 生成config#无需生成config
|
||
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
|
||
logger.info("Use gpus:", gpus16)
|
||
if pretrained_G14 == "":
|
||
logger.info("No pretrained Generator")
|
||
if pretrained_D15 == "":
|
||
logger.info("No pretrained Discriminator")
|
||
if gpus16:
|
||
cmd = get_quoted_python_cmd() + ' 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' % (
|
||
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 = (
|
||
config.python_cmd
|
||
+ ' 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'
|
||
% (
|
||
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"
|
||
|
||
|
||
# but4.click(train_index, [exp_dir1], info3)
|
||
def train_index(exp_dir1, version19):
|
||
# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
||
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)
|
||
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%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)
|
||
)
|
||
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
||
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
|
||
yield "\n".join(infos)
|
||
|
||
|
||
# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
|
||
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)
|
||
|
||
####### step1:处理数据
|
||
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)]
|
||
|
||
####### step2a:提取音高
|
||
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
|
||
)
|
||
]
|
||
|
||
####### step3a:训练模型
|
||
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"))
|
||
|
||
####### step3b:训练索引
|
||
[get_info_str(_) for _ in train_index(exp_dir1, version19)]
|
||
yield get_info_str(i18n("全流程结束！"))
|
||
|
||
|
||
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
||
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"}
|
||
|
||
|
||
with gr.Blocks(title="RVC WebUI") as app:
|
||
gr.Markdown(
|
||
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))
|
||
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.Group():
|
||
gr.Markdown(
|
||
value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
|
||
)
|
||
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)"),
|
||
value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
|
||
)
|
||
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="pm",
|
||
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,
|
||
)
|
||
with gr.Column():
|
||
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,
|
||
)
|
||
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,
|
||
)
|
||
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,
|
||
# )
|
||
index_rate1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=0.75,
|
||
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,
|
||
)
|
||
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"))
|
||
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.Group():
|
||
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")
|
||
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,
|
||
)
|
||
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,
|
||
)
|
||
with gr.Column():
|
||
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,
|
||
)
|
||
refresh_button.click(
|
||
fn=lambda: change_choices()[1],
|
||
inputs=[],
|
||
outputs=file_index4,
|
||
api_name="infer_refresh_batch",
|
||
)
|
||
# file_big_npy2 = 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,
|
||
# )
|
||
index_rate2 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=1,
|
||
interactive=True,
|
||
)
|
||
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,
|
||
)
|
||
with gr.Column():
|
||
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="E:\codes\py39\\test-20230416b\\todo-songs",
|
||
)
|
||
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="flac",
|
||
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_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],
|
||
)
|
||
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br> (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br> (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
|
||
)
|
||
)
|
||
with gr.Row():
|
||
with gr.Column():
|
||
dir_wav_input = gr.Textbox(
|
||
label=i18n("输入待处理音频文件夹路径"),
|
||
value="E:\\codes\\py39\\test-20230416b\\todo-songs\\todo-songs",
|
||
)
|
||
wav_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.Column():
|
||
model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
|
||
agg = gr.Slider(
|
||
minimum=0,
|
||
maximum=20,
|
||
step=1,
|
||
label="人声提取激进程度",
|
||
value=10,
|
||
interactive=True,
|
||
visible=False, # 先不开放调整
|
||
)
|
||
opt_vocal_root = gr.Textbox(
|
||
label=i18n("指定输出主人声文件夹"), value="opt"
|
||
)
|
||
opt_ins_root = gr.Textbox(
|
||
label=i18n("指定输出非主人声文件夹"), value="opt"
|
||
)
|
||
format0 = gr.Radio(
|
||
label=i18n("导出文件格式"),
|
||
choices=["wav", "flac", "mp3", "m4a"],
|
||
value="flac",
|
||
interactive=True,
|
||
)
|
||
but2 = gr.Button(i18n("转换"), variant="primary")
|
||
vc_output4 = gr.Textbox(label=i18n("输出信息"))
|
||
but2.click(
|
||
uvr,
|
||
[
|
||
model_choose,
|
||
dir_wav_input,
|
||
opt_vocal_root,
|
||
wav_inputs,
|
||
opt_ins_root,
|
||
agg,
|
||
format0,
|
||
],
|
||
[vc_output4],
|
||
api_name="uvr_convert",
|
||
)
|
||
with gr.TabItem(i18n("训练")):
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
|
||
sr2 = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
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,
|
||
)
|
||
version19 = gr.Radio(
|
||
label=i18n("版本"),
|
||
choices=["v1", "v2"],
|
||
value="v2",
|
||
interactive=True,
|
||
visible=True,
|
||
)
|
||
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,
|
||
)
|
||
with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
trainset_dir4 = gr.Textbox(
|
||
label=i18n("输入训练文件夹路径"), value="E:\\语音音频+标注\\米津玄师\\src"
|
||
)
|
||
spk_id5 = gr.Slider(
|
||
minimum=0,
|
||
maximum=4,
|
||
step=1,
|
||
label=i18n("请指定说话人id"),
|
||
value=0,
|
||
interactive=True,
|
||
)
|
||
but1 = gr.Button(i18n("处理数据"), variant="primary")
|
||
info1 = gr.Textbox(label=i18n("输出信息"), value="")
|
||
but1.click(
|
||
preprocess_dataset,
|
||
[trainset_dir4, exp_dir1, sr2, np7],
|
||
[info1],
|
||
api_name="train_preprocess",
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
|
||
with gr.Row():
|
||
with gr.Column():
|
||
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
|
||
)
|
||
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.Group():
|
||
gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
|
||
with gr.Row():
|
||
save_epoch10 = gr.Slider(
|
||
minimum=1,
|
||
maximum=50,
|
||
step=1,
|
||
label=i18n("保存频率save_every_epoch"),
|
||
value=5,
|
||
interactive=True,
|
||
)
|
||
total_epoch11 = gr.Slider(
|
||
minimum=2,
|
||
maximum=1000,
|
||
step=1,
|
||
label=i18n("总训练轮数total_epoch"),
|
||
value=20,
|
||
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("否"),
|
||
interactive=True,
|
||
)
|
||
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("否"),
|
||
interactive=True,
|
||
)
|
||
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,
|
||
)
|
||
pretrained_D15 = gr.Textbox(
|
||
label=i18n("加载预训练底模D路径"),
|
||
value="assets/pretrained_v2/f0D40k.pth",
|
||
interactive=True,
|
||
)
|
||
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],
|
||
)
|
||
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=gpus,
|
||
interactive=True,
|
||
)
|
||
but3 = gr.Button(i18n("训练模型"), variant="primary")
|
||
but4 = gr.Button(i18n("训练特征索引"), variant="primary")
|
||
but5 = gr.Button(i18n("一键训练"), variant="primary")
|
||
info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
|
||
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",
|
||
)
|
||
|
||
with gr.TabItem(i18n("ckpt处理")):
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
|
||
with gr.Row():
|
||
ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True)
|
||
ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True)
|
||
alpha_a = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("A模型权重"),
|
||
value=0.5,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
sr_ = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
if_f0_ = gr.Radio(
|
||
label=i18n("模型是否带音高指导"),
|
||
choices=[i18n("是"), i18n("否")],
|
||
value=i18n("是"),
|
||
interactive=True,
|
||
)
|
||
info__ = gr.Textbox(
|
||
label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
|
||
)
|
||
name_to_save0 = gr.Textbox(
|
||
label=i18n("保存的模型名不带后缀"),
|
||
value="",
|
||
max_lines=1,
|
||
interactive=True,
|
||
)
|
||
version_2 = gr.Radio(
|
||
label=i18n("模型版本型号"),
|
||
choices=["v1", "v2"],
|
||
value="v1",
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
but6 = gr.Button(i18n("融合"), variant="primary")
|
||
info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but6.click(
|
||
merge,
|
||
[
|
||
ckpt_a,
|
||
ckpt_b,
|
||
alpha_a,
|
||
sr_,
|
||
if_f0_,
|
||
info__,
|
||
name_to_save0,
|
||
version_2,
|
||
],
|
||
info4,
|
||
api_name="ckpt_merge",
|
||
) # def merge(path1,path2,alpha1,sr,f0,info):
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)"))
|
||
with gr.Row():
|
||
ckpt_path0 = gr.Textbox(
|
||
label=i18n("模型路径"), value="", interactive=True
|
||
)
|
||
info_ = gr.Textbox(
|
||
label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True
|
||
)
|
||
name_to_save1 = gr.Textbox(
|
||
label=i18n("保存的文件名, 默认空为和源文件同名"),
|
||
value="",
|
||
max_lines=8,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
but7 = gr.Button(i18n("修改"), variant="primary")
|
||
info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but7.click(
|
||
change_info,
|
||
[ckpt_path0, info_, name_to_save1],
|
||
info5,
|
||
api_name="ckpt_modify",
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)"))
|
||
with gr.Row():
|
||
ckpt_path1 = gr.Textbox(
|
||
label=i18n("模型路径"), value="", interactive=True
|
||
)
|
||
but8 = gr.Button(i18n("查看"), variant="primary")
|
||
info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况"
|
||
)
|
||
)
|
||
with gr.Row():
|
||
ckpt_path2 = gr.Textbox(
|
||
label=i18n("模型路径"),
|
||
value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
|
||
interactive=True,
|
||
)
|
||
save_name = gr.Textbox(
|
||
label=i18n("保存名"), value="", interactive=True
|
||
)
|
||
sr__ = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["32k", "40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
if_f0__ = gr.Radio(
|
||
label=i18n("模型是否带音高指导,1是0否"),
|
||
choices=["1", "0"],
|
||
value="1",
|
||
interactive=True,
|
||
)
|
||
version_1 = gr.Radio(
|
||
label=i18n("模型版本型号"),
|
||
choices=["v1", "v2"],
|
||
value="v2",
|
||
interactive=True,
|
||
)
|
||
info___ = gr.Textbox(
|
||
label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
|
||
)
|
||
but9 = gr.Button(i18n("提取"), variant="primary")
|
||
info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
ckpt_path2.change(
|
||
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
|
||
)
|
||
but9.click(
|
||
extract_small_model,
|
||
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
|
||
info7,
|
||
api_name="ckpt_extract",
|
||
)
|
||
|
||
with gr.TabItem(i18n("Onnx导出")):
|
||
with gr.Row():
|
||
ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
|
||
with gr.Row():
|
||
onnx_dir = gr.Textbox(
|
||
label=i18n("Onnx输出路径"), value="", interactive=True
|
||
)
|
||
with gr.Row():
|
||
infoOnnx = gr.Label(label="info")
|
||
with gr.Row():
|
||
butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
|
||
butOnnx.click(
|
||
export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx"
|
||
)
|
||
|
||
tab_faq = i18n("常见问题解答")
|
||
with gr.TabItem(tab_faq):
|
||
try:
|
||
if tab_faq == "常见问题解答":
|
||
with open("docs/cn/faq.md", "r", encoding="utf8") as f:
|
||
info = f.read()
|
||
else:
|
||
with open("docs/en/faq_en.md", "r", encoding="utf8") as f:
|
||
info = f.read()
|
||
gr.Markdown(value=info)
|
||
except:
|
||
gr.Markdown(traceback.format_exc())
|
||
|
||
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,
|
||
)
|