From 2ee76f8ce0719bc24602fc839fa2c0240bdde72a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=CE=9D=CE=B1=CF=81=CE=BF=CF=85=CF=83=CE=AD=C2=B7=CE=BC?=
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<40709280+NaruseMioShirakana@users.noreply.github.com>
Date: Mon, 24 Apr 2023 19:23:00 +0800
Subject: [PATCH] Add files via upload
---
export_onnx.py | 79 +--
infer-web.py | 1499 +++++++++++++-----------------------------------
2 files changed, 404 insertions(+), 1174 deletions(-)
diff --git a/export_onnx.py b/export_onnx.py
index 35f217f..80f061b 100644
--- a/export_onnx.py
+++ b/export_onnx.py
@@ -1,44 +1,36 @@
-from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsid
-from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
+from infer_pack.models_onnx import SynthesizerTrnMs256NSFsid
import torch
-if __name__ == '__main__':
- MoeVS = True #模型是否为MoeVoiceStudio（原MoeSS）使用
+person = "Shiroha/shiroha.pth"
+exported_path = "model.onnx"
- ModelPath = "Shiroha/shiroha.pth" #模型路径
- ExportedPath = "model.onnx" #输出路径
- hidden_channels = 256 # hidden_channels，为768Vec做准备
- cpt = torch.load(person, map_location="cpu")
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- print(*cpt["config"])
- test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
- test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
- test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
- test_pitchf = torch.rand(1, 200) # nsf基频
- test_ds = torch.LongTensor([0]) # 说话人ID
- test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
- device = "cpu" #导出时设备（不影响使用模型）
+cpt = torch.load(person, map_location="cpu")
+cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
+print(*cpt["config"])
+net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False)
+net_g.load_state_dict(cpt["weight"], strict=False)
- if MoeVS:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
- net_g.load_state_dict(cpt["weight"], strict=False)
- input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
- output_names = [
- "audio",
- ]
- torch.onnx.export(
- net_g,
+test_phone = torch.rand(1, 200, 256)
+test_phone_lengths = torch.tensor([200]).long()
+test_pitch = torch.randint(size=(1 ,200),low=5,high=255)
+test_pitchf = torch.rand(1, 200)
+test_ds = torch.LongTensor([0])
+test_rnd = torch.rand(1, 192, 200)
+input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
+output_names = ["audio", ]
+device="cpu"
+torch.onnx.export(net_g,
(
test_phone.to(device),
test_phone_lengths.to(device),
test_pitch.to(device),
test_pitchf.to(device),
test_ds.to(device),
- test_rnd.to(device),
+ test_rnd.to(device)
),
- ExportedPath,
+ exported_path,
dynamic_axes={
"phone": [1],
"pitch": [1],
@@ -49,33 +41,4 @@ if __name__ == '__main__':
opset_version=16,
verbose=False,
input_names=input_names,
- output_names=output_names,
- )
- else:
- net_g = SynthesizerTrnMs256NSFsidO(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
- net_g.load_state_dict(cpt["weight"], strict=False)
- input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
- output_names = [
- "audio",
- ]
- torch.onnx.export(
- net_g,
- (
- test_phone.to(device),
- test_phone_lengths.to(device),
- test_pitch.to(device),
- test_pitchf.to(device),
- test_ds.to(device),
- ),
- ExportedPath,
- dynamic_axes={
- "phone": [1],
- "pitch": [1],
- "pitchf": [1],
- },
- do_constant_folding=False,
- opset_version=16,
- verbose=False,
- input_names=input_names,
- output_names=output_names,
- )
\ No newline at end of file
+ output_names=output_names)
\ No newline at end of file
diff --git a/infer-web.py b/infer-web.py
index b027f0e..751bf0f 100644
--- a/infer-web.py
+++ b/infer-web.py
@@ -3,258 +3,132 @@ import threading
from time import sleep
from subprocess import Popen
from time import sleep
-import torch, os, traceback, sys, warnings, shutil, numpy as np
+import torch, os,traceback,sys,warnings,shutil,numpy as np
import faiss
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-tmp = os.path.join(now_dir, "TEMP")
-shutil.rmtree(tmp, ignore_errors=True)
-os.makedirs(tmp, exist_ok=True)
-os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
-os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
-os.environ["TEMP"] = tmp
-warnings.filterwarnings("ignore")
-torch.manual_seed(114514)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-# 判断是否有能用来训练和加速推理的N卡
-ncpu = cpu_count()
-ngpu = torch.cuda.device_count()
-gpu_infos = []
-if (not torch.cuda.is_available()) or ngpu == 0:
- if_gpu_ok = False
+#判断是否有能用来训练和加速推理的N卡
+ncpu=cpu_count()
+ngpu=torch.cuda.device_count()
+gpu_infos=[]
+if(torch.cuda.is_available()==False or ngpu==0):if_gpu_ok=False
else:
if_gpu_ok = False
for i in range(ngpu):
- gpu_name = torch.cuda.get_device_name(i)
- if ("16" in gpu_name and "V100" not in gpu_name) or "MX" in gpu_name:
- continue
- if (
- "10" in gpu_name
- or "20" in gpu_name
- or "30" in gpu_name
- or "40" in gpu_name
- or "A2" in gpu_name.upper()
- or "A3" in gpu_name.upper()
- or "A4" in gpu_name.upper()
- or "P4" in gpu_name.upper()
- or "A50" in gpu_name.upper()
- or "70" in gpu_name
- or "80" in gpu_name
- or "90" in gpu_name
- or "M4" in gpu_name
- or "T4" in gpu_name
- or "TITAN" in gpu_name.upper()
- ): # A10#A100#V100#A40#P40#M40#K80
- if_gpu_ok = True # 至少有一张能用的N卡
- gpu_infos.append("%s\t%s" % (i, gpu_name))
-gpu_info = (
- "\n".join(gpu_infos)
- if if_gpu_ok == True and len(gpu_infos) > 0
- else "很遗憾您这没有能用的显卡来支持您训练"
-)
-gpus = "-".join([i[0] for i in gpu_infos])
+ gpu_name=torch.cuda.get_device_name(i)
+ if("16"in gpu_name or "MX"in gpu_name):continue
+ if("10"in gpu_name or "20"in gpu_name or "30"in gpu_name or "40"in gpu_name or "A50"in gpu_name.upper() or "70"in gpu_name or "80"in gpu_name or "90"in gpu_name or "M4"in gpu_name or "T4"in gpu_name or "TITAN"in gpu_name.upper()):#A10#A100#V100#A40#P40#M40#K80
+ if_gpu_ok=True#至少有一张能用的N卡
+ gpu_infos.append("%s\t%s"%(i,gpu_name))
+gpu_info="\n".join(gpu_infos)if if_gpu_ok==True and len(gpu_infos)>0 else "很遗憾您这没有能用的显卡来支持您训练"
+gpus="-".join([i[0]for i in gpu_infos])
+now_dir=os.getcwd()
+sys.path.append(now_dir)
+tmp=os.path.join(now_dir,"TEMP")
+shutil.rmtree(tmp,ignore_errors=True)
+os.makedirs(tmp,exist_ok=True)
+os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
+os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
+os.environ["TEMP"]=tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
from scipy.io import wavfile
from fairseq import checkpoint_utils
import gradio as gr
import logging
from vc_infer_pipeline import VC
-from config import (
- is_half,
- device,
- python_cmd,
- listen_port,
- iscolab,
- noparallel,
- noautoopen,
-)
+from config import is_half,device,is_half,python_cmd,listen_port,iscolab,noparallel
from infer_uvr5 import _audio_pre_
from my_utils import load_audio
-from train.process_ckpt import show_info, change_info, merge, extract_small_model
-
+from train.process_ckpt import show_info,change_info,merge,extract_small_model
# from trainset_preprocess_pipeline import PreProcess
-logging.getLogger("numba").setLevel(logging.WARNING)
-
+logging.getLogger('numba').setLevel(logging.WARNING)
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
-
def __init__(self, **kwargs):
super().__init__(variant="tool", **kwargs)
-
def get_block_name(self):
return "button"
-
-hubert_model = None
-
-
+hubert_model=None
def load_hubert():
global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
+ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
hubert_model = models[0]
hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
+ if(is_half):hubert_model = hubert_model.half()
+ else:hubert_model = hubert_model.float()
hubert_model.eval()
-
-weight_root = "weights"
-weight_uvr5_root = "uvr5_weights"
-names = []
+weight_root="weights"
+weight_uvr5_root="uvr5_weights"
+names=[]
for name in os.listdir(weight_root):
- if name.endswith(".pth"):
- names.append(name)
-uvr5_names = []
+ if name.endswith(".pth"): names.append(name)
+uvr5_names=[]
for name in os.listdir(weight_uvr5_root):
- if name.endswith(".pth"):
- uvr5_names.append(name.replace(".pth", ""))
+ if name.endswith(".pth"): uvr5_names.append(name.replace(".pth",""))
-
-def vc_single(
- sid,
- input_audio,
- f0_up_key,
- f0_file,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
-): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
- global tgt_sr, net_g, vc, hubert_model
- if input_audio is None:
- return "You need to upload an audio", None
+def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_npy,index_rate):#spk_item, input_audio0, vc_transform0,f0_file,f0method0
+ global tgt_sr,net_g,vc,hubert_model
+ if input_audio is None:return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
try:
- audio = load_audio(input_audio, 16000)
+ audio=load_audio(input_audio,16000)
times = [0, 0, 0]
- if hubert_model == None:
- load_hubert()
+ if(hubert_model==None):load_hubert()
if_f0 = cpt.get("f0", 1)
- file_index = (
- file_index.strip(" ")
- .strip('"')
- .strip("\n")
- .strip('"')
- .strip(" ")
- .replace("trained", "added")
- ) # 防止小白写错，自动帮他替换掉
- file_big_npy = (
- file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- )
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- sid,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- f0_file=f0_file,
- )
- print(
- "npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
- )
+ audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
+ print(times)
return "Success", (tgt_sr, audio_opt)
except:
- info = traceback.format_exc()
+ info=traceback.format_exc()
print(info)
- return info, (None, None)
+ return info,(None,None)
-
-def vc_multi(
- sid,
- dir_path,
- opt_root,
- paths,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
-):
+def vc_multi(sid,dir_path,opt_root,paths,f0_up_key,f0_method,file_index,file_big_npy,index_rate):
try:
- dir_path = (
- dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- ) # 防止小白拷路径头尾带了空格和"和回车
- opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ dir_path=dir_path.strip(" ")#防止小白拷路径头尾带了空格
+ opt_root=opt_root.strip(" ")
os.makedirs(opt_root, exist_ok=True)
try:
- if dir_path != "":
- paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
- else:
- paths = [path.name for path in paths]
+ if(dir_path!=""):paths=[os.path.join(dir_path,name)for name in os.listdir(dir_path)]
+ else:paths=[path.name for path in paths]
except:
traceback.print_exc()
paths = [path.name for path in paths]
- infos = []
+ infos=[]
for path in paths:
- info, opt = vc_single(
- sid,
- path,
- f0_up_key,
- None,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- )
- if info == "Success":
+ info,opt=vc_single(sid,path,f0_up_key,None,f0_method,file_index,file_big_npy,index_rate)
+ if(info=="Success"):
try:
- tgt_sr, audio_opt = opt
- wavfile.write(
- "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
- )
+ tgt_sr,audio_opt=opt
+ wavfile.write("%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt)
except:
- info = traceback.format_exc()
- infos.append("%s->%s" % (os.path.basename(path), info))
+ info=traceback.format_exc()
+ infos.append("%s->%s"%(os.path.basename(path),info))
yield "\n".join(infos)
yield "\n".join(infos)
except:
yield traceback.format_exc()
-
-def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins):
+def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins):
infos = []
try:
- inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- save_root_vocal = (
- save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- )
- save_root_ins = (
- save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- )
- pre_fun = _audio_pre_(
- model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
- device=device,
- is_half=is_half,
- )
- if inp_root != "":
- paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
- else:
- paths = [path.name for path in paths]
+ inp_root = inp_root.strip(" ").strip("\n")
+ save_root_vocal = save_root_vocal.strip(" ").strip("\n")
+ save_root_ins = save_root_ins.strip(" ").strip("\n")
+ pre_fun = _audio_pre_(model_path=os.path.join(weight_uvr5_root,model_name+".pth"), device=device, is_half=is_half)
+ if (inp_root != ""):paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
+ else:paths = [path.name for path in paths]
for name in paths:
- inp_path = os.path.join(inp_root, name)
+ inp_path=os.path.join(inp_root,name)
try:
- pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
- infos.append("%s->Success" % (os.path.basename(inp_path)))
+ pre_fun._path_audio_(inp_path , save_root_ins,save_root_vocal)
+ infos.append("%s->Success"%(os.path.basename(inp_path)))
yield "\n".join(infos)
except:
- infos.append(
- "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
- )
+ infos.append("%s->%s" % (os.path.basename(inp_path),traceback.format_exc()))
yield "\n".join(infos)
except:
infos.append(traceback.format_exc())
@@ -266,1112 +140,505 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins):
except:
traceback.print_exc()
print("clean_empty_cache")
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
+ torch.cuda.empty_cache()
yield "\n".join(infos)
-
-# 一个选项卡全局只能有一个音色
+#一个选项卡全局只能有一个音色
def get_vc(sid):
- global n_spk, tgt_sr, net_g, vc, cpt
- if sid == []:
+ global n_spk,tgt_sr,net_g,vc,cpt
+ if(sid==[]):
global hubert_model
- if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+ if (hubert_model != None): # 考虑到轮询，需要加个判断看是否 sid 是由有模型切换到无模型的
print("clean_empty_cache")
- del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
- hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
+ del net_g, n_spk, vc, hubert_model,tgt_sr#,cpt
+ hubert_model = net_g=n_spk=vc=hubert_model=tgt_sr=None
+ torch.cuda.empty_cache()
###楼下不这么折腾清理不干净
if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
+ if (if_f0 == 1):
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g, cpt
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- cpt = None
+ del net_g,cpt
+ torch.cuda.empty_cache()
+ cpt=None
return {"visible": False, "__type__": "update"}
person = "%s/%s" % (weight_root, sid)
- print("loading %s" % person)
+ print("loading %s"%person)
cpt = torch.load(person, map_location="cpu")
tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
+ cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
+ if_f0=cpt.get("f0",1)
+ if(if_f0==1):
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
+ print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩
net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
+ if (is_half):net_g = net_g.half()
+ else:net_g = net_g.float()
vc = VC(tgt_sr, device, is_half)
- n_spk = cpt["config"][-3]
- return {"visible": True, "maximum": n_spk, "__type__": "update"}
-
+ n_spk=cpt["config"][-3]
+ return {"visible": True,"maximum": n_spk, "__type__": "update"}
def change_choices():
- names = []
+ names=[]
for name in os.listdir(weight_root):
- if name.endswith(".pth"):
- names.append(name)
+ if name.endswith(".pth"): names.append(name)
return {"choices": sorted(names), "__type__": "update"}
+def clean():return {"value": "", "__type__": "update"}
+def change_f0(if_f0_3,sr2):#np7, f0method8,pretrained_G14,pretrained_D15
+ if(if_f0_3=="是"):return {"visible": True, "__type__": "update"},{"visible": True, "__type__": "update"},"pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2
+ return {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"},"pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2
-
-def clean():
- return {"value": "", "__type__": "update"}
-
-
-def change_f0(if_f0_3, sr2): # np7, f0method8,pretrained_G14,pretrained_D15
- if if_f0_3 == "是":
- return (
- {"visible": True, "__type__": "update"},
- {"visible": True, "__type__": "update"},
- "pretrained/f0G%s.pth" % sr2,
- "pretrained/f0D%s.pth" % sr2,
- )
- return (
- {"visible": False, "__type__": "update"},
- {"visible": False, "__type__": "update"},
- "pretrained/G%s.pth" % sr2,
- "pretrained/D%s.pth" % sr2,
- )
-
-
-sr_dict = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
+sr_dict={
+ "32k":32000,
+ "40k":40000,
+ "48k":48000,
}
-
-def if_done(done, p):
+def if_done(done,p):
while 1:
- if p.poll() == None:
- sleep(0.5)
- else:
- break
- done[0] = True
+ if(p.poll()==None):sleep(0.5)
+ else:break
+ done[0]=True
-def if_done_multi(done, ps):
+def if_done_multi(done,ps):
while 1:
- # poll==None代表进程未结束
- # 只要有一个进程未结束都不停
- flag = 1
+ #poll==None代表进程未结束
+ #只要有一个进程未结束都不停
+ flag=1
for p in ps:
- if p.poll() == None:
+ if(p.poll()==None):
flag = 0
sleep(0.5)
break
- if flag == 1:
- break
- done[0] = True
+ if(flag==1):break
+ done[0]=True
-
-def preprocess_dataset(trainset_dir, exp_dir, sr, n_p=ncpu):
- sr = sr_dict[sr]
- os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
- f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
+def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu):
+ sr=sr_dict[sr]
+ os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
+ f = open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "w")
f.close()
- cmd = (
- python_cmd
- + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
- % (trainset_dir, sr, n_p, now_dir, exp_dir)
- + str(noparallel)
- )
+ cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir,sr,n_p,now_dir,exp_dir)+str(noparallel)
print(cmd)
- p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
- ###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
- done = [False]
- threading.Thread(
- target=if_done,
- args=(
- done,
- p,
- ),
- ).start()
- while 1:
- with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
- yield (f.read())
+ p = Popen(cmd, shell=True)#, stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
+ ###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
+ done=[False]
+ threading.Thread(target=if_done,args=(done,p,)).start()
+ while(1):
+ with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir),"r")as f:yield(f.read())
sleep(1)
- if done[0] == True:
- break
- with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
- log = f.read()
+ if(done[0]==True):break
+ with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "r")as f:log = f.read()
print(log)
yield log
-
-
-# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
-def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
- gpus = gpus.split("-")
- os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
- f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
+#but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
+def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
+ gpus=gpus.split("-")
+ os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
+ f = open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "w")
f.close()
- if if_f0 == "是":
- cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
- now_dir,
- exp_dir,
- n_p,
- f0method,
- )
+ if(if_f0=="是"):
+ cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
print(cmd)
- p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
- ###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
- done = [False]
- threading.Thread(
- target=if_done,
- args=(
- done,
- p,
- ),
- ).start()
- while 1:
- with open(
- "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
- ) as f:
- yield (f.read())
+ p = Popen(cmd, shell=True,cwd=now_dir)#, stdin=PIPE, stdout=PIPE,stderr=PIPE
+ ###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
+ done=[False]
+ threading.Thread(target=if_done,args=(done,p,)).start()
+ while(1):
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir),"r")as f:yield(f.read())
sleep(1)
- if done[0] == True:
- break
- with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
- log = f.read()
+ if(done[0]==True):break
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read()
print(log)
yield log
####对不同part分别开多进程
- """
+ '''
n_part=int(sys.argv[1])
i_part=int(sys.argv[2])
i_gpu=sys.argv[3]
exp_dir=sys.argv[4]
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
- """
- leng = len(gpus)
- ps = []
- for idx, n_g in enumerate(gpus):
- cmd = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
- device,
- leng,
- idx,
- n_g,
- now_dir,
- exp_dir,
- )
+ '''
+ leng=len(gpus)
+ ps=[]
+ for idx,n_g in enumerate(gpus):
+ cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
print(cmd)
- p = Popen(
- cmd, shell=True, cwd=now_dir
- ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+ p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
- ###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
+ ###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
done = [False]
- threading.Thread(
- target=if_done_multi,
- args=(
- done,
- ps,
- ),
- ).start()
- while 1:
- with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
- yield (f.read())
+ threading.Thread(target=if_done_multi, args=(done, ps,)).start()
+ while (1):
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:yield (f.read())
sleep(1)
- if done[0] == True:
- break
- with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
- log = f.read()
+ if (done[0] == True): break
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read()
print(log)
yield log
-
-
-def change_sr2(sr2, if_f0_3):
- if if_f0_3 == "是":
- return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
- else:
- return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
-
-
-# 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,
-):
- # 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)
- co256_dir = "%s/3_feature256" % (exp_dir)
- if if_f0_3 == "是":
+def change_sr2(sr2,if_f0_3):
+ if(if_f0_3=="是"):return "pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2
+ else:return "pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2
+#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):
+ #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)
+ co256_dir="%s/3_feature256"%(exp_dir)
+ if(if_f0_3=="是"):
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(co256_dir)])
- & set([name.split(".")[0] for name in os.listdir(f0_dir)])
- & set([name.split(".")[0] for name in os.listdir(f0nsf_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(co256_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(co256_dir)]
- )
- opt = []
+ names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])
+ opt=[]
for name in names:
- if if_f0_3 == "是":
- opt.append(
- "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
- % (
- gt_wavs_dir.replace("\\", "\\\\"),
- name,
- co256_dir.replace("\\", "\\\\"),
- name,
- f0_dir.replace("\\", "\\\\"),
- name,
- f0nsf_dir.replace("\\", "\\\\"),
- name,
- spk_id5,
- )
- )
+ if (if_f0_3 == "是"):
+ opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_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,
- co256_dir.replace("\\", "\\\\"),
- name,
- spk_id5,
- )
- )
- if if_f0_3 == "是":
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
- % (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
- )
- else:
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
- % (now_dir, sr2, now_dir, spk_id5)
- )
- with open("%s/filelist.txt" % exp_dir, "w") as f:
- f.write("\n".join(opt))
+ opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
+ with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
print("write filelist done")
- # 生成config#无需生成config
+ #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"
- print("use gpus:", gpus16)
+ print("use gpus:",gpus16)
if gpus16:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- gpus16,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
+ cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
else:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
+ cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
print(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"
-
-
+ 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):
- exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
- os.makedirs(exp_dir, exist_ok=True)
- feature_dir = "%s/3_feature256" % (exp_dir)
- if os.path.exists(feature_dir) == False:
- 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！"
+ exp_dir="%s/logs/%s"%(now_dir,exp_dir1)
+ os.makedirs(exp_dir,exist_ok=True)
+ feature_dir="%s/3_feature256"%(exp_dir)
+ if(os.path.exists(feature_dir)==False):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！"
npys = []
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
- np.save("%s/total_fea.npy" % exp_dir, big_npy)
+ np.save("%s/total_fea.npy"%exp_dir, big_npy)
n_ivf = big_npy.shape[0] // 39
- infos = []
- infos.append("%s,%s" % (big_npy.shape, n_ivf))
+ infos=[]
+ infos.append("%s,%s"%(big_npy.shape,n_ivf))
yield "\n".join(infos)
- index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
+ index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf)
infos.append("training")
yield "\n".join(infos)
index_ivf = faiss.extract_index_ivf(index) #
- index_ivf.nprobe = int(np.power(n_ivf, 0.3))
+ index_ivf.nprobe = int(np.power(n_ivf,0.3))
index.train(big_npy)
- faiss.write_index(
- index,
- "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
- )
+ faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
infos.append("adding")
yield "\n".join(infos)
index.add(big_npy)
- faiss.write_index(
- index,
- "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
- )
- infos.append("成功构建索引, added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe))
+ faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
+ infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe))
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,
- gpus6,
- np7,
- f0method8,
- save_epoch10,
- total_epoch11,
- batch_size12,
- if_save_latest13,
- pretrained_G14,
- pretrained_D15,
- gpus16,
- if_cache_gpu17,
-):
- 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, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17):
+ infos=[]
def get_info_str(strr):
infos.append(strr)
return "\n".join(infos)
-
- os.makedirs("%s/logs/%s" % (now_dir, exp_dir1), exist_ok=True)
+ os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True)
#########step1:处理数据
- open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "w").close()
- cmd = (
- python_cmd
- + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
- % (trainset_dir4, sr_dict[sr2], ncpu, now_dir, exp_dir1)
- + str(noparallel)
- )
+ open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close()
+ cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)+str(noparallel)
yield get_info_str("step1:正在处理数据")
yield get_info_str(cmd)
p = Popen(cmd, shell=True)
p.wait()
- with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r") as f:
- print(f.read())
+ with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r")as f: print(f.read())
#########step2a:提取音高
open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
- if if_f0_3 == "是":
+ if(if_f0_3=="是"):
yield get_info_str("step2a:正在提取音高")
- cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
- now_dir,
- exp_dir1,
- np7,
- f0method8,
- )
+ cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
yield get_info_str(cmd)
- p = Popen(cmd, shell=True, cwd=now_dir)
+ p = Popen(cmd, shell=True,cwd=now_dir)
p.wait()
- with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "r") as f:
- print(f.read())
- else:
- yield get_info_str("step2a:无需提取音高")
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read())
+ else:yield get_info_str("step2a:无需提取音高")
#######step2b:提取特征
yield get_info_str("step2b:正在提取特征")
- gpus = gpus16.split("-")
- leng = len(gpus)
- ps = []
- for idx, n_g in enumerate(gpus):
- cmd = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
- device,
- leng,
- idx,
- n_g,
- now_dir,
- exp_dir1,
- )
+ gpus=gpus16.split("-")
+ leng=len(gpus)
+ ps=[]
+ for idx,n_g in enumerate(gpus):
+ cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
yield get_info_str(cmd)
- p = Popen(
- cmd, shell=True, cwd=now_dir
- ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+ p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
- for p in ps:
- p.wait()
- with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "r") as f:
- print(f.read())
+ for p in ps:p.wait()
+ with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read())
#######step3a:训练模型
yield get_info_str("step3a:正在训练模型")
- # 生成filelist
- exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
- gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
- co256_dir = "%s/3_feature256" % (exp_dir)
- if if_f0_3 == "是":
+ #生成filelist
+ exp_dir="%s/logs/%s"%(now_dir,exp_dir1)
+ gt_wavs_dir="%s/0_gt_wavs"%(exp_dir)
+ co256_dir="%s/3_feature256"%(exp_dir)
+ if(if_f0_3=="是"):
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(co256_dir)])
- & set([name.split(".")[0] for name in os.listdir(f0_dir)])
- & set([name.split(".")[0] for name in os.listdir(f0nsf_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(co256_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(co256_dir)]
- )
- opt = []
+ names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])
+ opt=[]
for name in names:
- if if_f0_3 == "是":
- opt.append(
- "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
- % (
- gt_wavs_dir.replace("\\", "\\\\"),
- name,
- co256_dir.replace("\\", "\\\\"),
- name,
- f0_dir.replace("\\", "\\\\"),
- name,
- f0nsf_dir.replace("\\", "\\\\"),
- name,
- spk_id5,
- )
- )
+ if (if_f0_3 == "是"):
+ opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_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,
- co256_dir.replace("\\", "\\\\"),
- name,
- spk_id5,
- )
- )
- if if_f0_3 == "是":
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
- % (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
- )
- else:
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
- % (now_dir, sr2, now_dir, spk_id5)
- )
- with open("%s/filelist.txt" % exp_dir, "w") as f:
- f.write("\n".join(opt))
+ opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
+ with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
yield get_info_str("write filelist done")
if gpus16:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- gpus16,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
+ cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
else:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
+ cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
yield get_info_str(cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
- yield get_info_str("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")
+ yield get_info_str("训练结束，您可查看控制台训练日志或实验文件夹下的train.log")
#######step3b:训练索引
- feature_dir = "%s/3_feature256" % (exp_dir)
+ feature_dir="%s/3_feature256"%(exp_dir)
npys = []
- listdir_res = list(os.listdir(feature_dir))
+ listdir_res=list(os.listdir(feature_dir))
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
- np.save("%s/total_fea.npy" % exp_dir, big_npy)
+ np.save("%s/total_fea.npy"%exp_dir, big_npy)
n_ivf = big_npy.shape[0] // 39
- yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
- index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
+ yield get_info_str("%s,%s"%(big_npy.shape,n_ivf))
+ index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf)
yield get_info_str("training index")
index_ivf = faiss.extract_index_ivf(index) #
- index_ivf.nprobe = int(np.power(n_ivf, 0.3))
+ index_ivf.nprobe = int(np.power(n_ivf,0.3))
index.train(big_npy)
- faiss.write_index(
- index,
- "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
- )
+ faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
yield get_info_str("adding index")
index.add(big_npy)
- faiss.write_index(
- index,
- "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
- )
- yield get_info_str(
- "成功构建索引, added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe)
- )
+ faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
+ yield get_info_str("成功构建索引，added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe))
yield get_info_str("全流程结束！")
-
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
def change_info_(ckpt_path):
- if (
- os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
- == False
- ):
- return {"__type__": "update"}, {"__type__": "update"}
+ if(os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"))==False):return {"__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"]
- return sr, str(f0)
+ 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"]
+ return sr,str(f0)
except:
traceback.print_exc()
return {"__type__": "update"}, {"__type__": "update"}
with gr.Blocks() 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.
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. 详见根目录使用需遵守的协议-LICENSE.txt."
- )
- )
+ gr.Markdown(value="""
+ 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。
+ 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"使用需遵守的协议-LICENSE.txt"。
+ """)
with gr.Tabs():
- with gr.TabItem(i18n("模型推理")):
+ with gr.TabItem("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"), variant="primary")
- refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
- 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,
+ sid0 = gr.Dropdown(label="推理音色", choices=sorted(names))
+ refresh_button = gr.Button("刷新音色列表", variant="primary")
+ refresh_button.click(
+ fn=change_choices,
+ inputs=[],
+ outputs=[sid0]
+ )
+ clean_button = gr.Button("卸载音色省显存", variant="primary")
+ spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label='请选择说话人id', value=0, visible=False, interactive=True)
+ clean_button.click(
+ fn=clean,
+ inputs=[],
+ outputs=[sid0]
)
- clean_button.click(fn=clean, inputs=[], outputs=[sid0])
sid0.change(
fn=get_vc,
inputs=[sid0],
outputs=[spk_item],
)
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. ")
- )
+ gr.Markdown(value="""
+ 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\\vits_vc_gpu_train\\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"),
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
+ vc_transform0 = gr.Number(label="变调（整数，半音数量，升八度12降八度-12）", value=0)
+ input_audio0 = gr.Textbox(label="输入待处理音频文件路径(默认是正确格式示例)",value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs\冬之花clip1.wav")
+ f0method0=gr.Radio(label="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", choices=["pm","harvest"],value="pm", interactive=True)
with gr.Column():
- file_index1 = gr.Textbox(
- label=i18n("特征检索库文件路径"),
- value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
- interactive=True,
- )
- 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="检索特征占比",
- value=0.6,
- 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")
+ file_index1 = gr.Textbox(label="特征检索库文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True)
+ file_big_npy1 = gr.Textbox(label="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='检索特征占比', value=1,interactive=True)
+ f0_file = gr.File(label="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("转换", variant="primary")
with gr.Column():
- 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_single,
- [
- spk_item,
- input_audio0,
- vc_transform0,
- f0_file,
- f0method0,
- file_index1,
- file_big_npy1,
- index_rate1,
- ],
- [vc_output1, vc_output2],
- )
+ vc_output1 = gr.Textbox(label="输出信息")
+ vc_output2 = gr.Audio(label="输出音频(右下角三个点,点了可以下载)")
+ but0.click(vc_single, [spk_item, input_audio0, vc_transform0,f0_file,f0method0,file_index1,file_big_npy1,index_rate1], [vc_output1, vc_output2])
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. ")
- )
+ gr.Markdown(value="""
+ 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"),
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
+ vc_transform1 = gr.Number(label="变调（整数，半音数量，升八度12降八度-12）", value=0)
+ opt_input = gr.Textbox(label="指定输出文件夹",value="opt")
+ f0method1=gr.Radio(label="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", choices=["pm","harvest"],value="pm", interactive=True)
with gr.Column():
- file_index2 = gr.Textbox(
- label=i18n("特征检索库文件路径"),
- value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
- interactive=True,
- )
- 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,
- )
+ file_index2 = gr.Textbox(label="特征检索库文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True)
+ file_big_npy2 = gr.Textbox(label="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='检索特征占比', value=1,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\\vits_vc_gpu_train\\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")
- )
- but1 = gr.Button(i18n("转换"), variant="primary")
- vc_output3 = gr.Textbox(label=i18n("输出信息"))
- but1.click(
- vc_multi,
- [
- spk_item,
- dir_input,
- opt_input,
- inputs,
- vc_transform1,
- f0method1,
- file_index2,
- file_big_npy2,
- index_rate2,
- ],
- [vc_output3],
- )
- with gr.TabItem(i18n("伴奏人声分离")):
+ dir_input = gr.Textbox(label="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\\vits_vc_gpu_train\\todo-songs")
+ inputs = gr.File(file_count="multiple", label="也可批量输入音频文件，二选一，优先读文件夹")
+ but1=gr.Button("转换", variant="primary")
+ vc_output3 = gr.Textbox(label="输出信息")
+ but1.click(vc_multi, [spk_item, dir_input,opt_input,inputs, vc_transform1,f0method1,file_index2,file_big_npy2,index_rate2], [vc_output3])
+ with gr.TabItem("伴奏人声分离"):
with gr.Group():
- gr.Markdown(
- value=i18n(
- "人声伴奏分离批量处理, 使用UVR5模型.
不带和声用HP2, 带和声且提取的人声不需要和声用HP5
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)"
- )
- )
+ gr.Markdown(value="""
+ 人声伴奏分离批量处理，使用UVR5模型。
+ 不带和声用HP2，带和声且提取的人声不需要和声用HP5
+ 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）
+ """)
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\\vits_vc_gpu_train\\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")
- )
+ dir_wav_input = gr.Textbox(label="输入待处理音频文件夹路径",value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs")
+ wav_inputs = gr.File(file_count="multiple", label="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)
- opt_vocal_root = gr.Textbox(
- label=i18n("指定输出人声文件夹"), value="opt"
- )
- opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"), value="opt")
- 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,
- ],
- [vc_output4],
- )
- 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. "
- )
- )
+ model_choose = gr.Dropdown(label="模型", choices=uvr5_names)
+ opt_vocal_root = gr.Textbox(label="指定输出人声文件夹",value="opt")
+ opt_ins_root = gr.Textbox(label="指定输出乐器文件夹",value="opt")
+ but2=gr.Button("转换", variant="primary")
+ vc_output4 = gr.Textbox(label="输出信息")
+ but2.click(uvr, [model_choose, dir_wav_input,opt_vocal_root,wav_inputs,opt_ins_root], [vc_output4])
+ with gr.TabItem("训练"):
+ gr.Markdown(value="""
+ 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=["32k", "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=["是", "否"],
- value="是",
- 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. "
- )
- )
+ exp_dir1 = gr.Textbox(label="输入实验名",value="mi-test")
+ sr2 = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+ if_f0_3 = gr.Radio(label="模型是否带音高指导(唱歌一定要，语音可以不要)", choices=["是","否"],value="是", 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="""
+ 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], [info1]
- )
+ trainset_dir4 = gr.Textbox(label="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='请指定说话人id', value=0,interactive=True)
+ but1=gr.Button("处理数据", variant="primary")
+ info1=gr.Textbox(label="输出信息",value="")
+ but1.click(preprocess_dataset,[trainset_dir4,exp_dir1,sr2],[info1])
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)"))
+ gr.Markdown(value="""
+ 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,
- )
- gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+ gpus6 = gr.Textbox(label="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)
+ gpu_info9 = gr.Textbox(label="显卡信息",value=gpu_info)
with gr.Column():
- np7 = gr.Slider(
- minimum=0,
- maximum=ncpu,
- step=1,
- label=i18n("提取音高使用的CPU进程数"),
- value=ncpu,
- interactive=True,
- )
- 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"
- ),
- choices=["pm", "harvest", "dio"],
- value="harvest",
- interactive=True,
- )
- but2 = gr.Button(i18n("特征提取"), variant="primary")
- info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
- but2.click(
- extract_f0_feature,
- [gpus6, np7, f0method8, if_f0_3, exp_dir1],
- [info2],
- )
+ np7 = gr.Slider(minimum=0, maximum=ncpu, step=1, label='提取音高使用的CPU进程数', value=ncpu,interactive=True)
+ f0method8 = gr.Radio(label="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", choices=["pm", "harvest","dio"], value="harvest", interactive=True)
+ but2=gr.Button("特征提取", variant="primary")
+ info2=gr.Textbox(label="输出信息",value="",max_lines=8)
+ but2.click(extract_f0_feature,[gpus6,np7,f0method8,if_f0_3,exp_dir1],[info2])
with gr.Group():
- gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
+ gr.Markdown(value="""
+ step3：填写训练设置，开始训练模型和索引
+ """)
with gr.Row():
- save_epoch10 = gr.Slider(
- minimum=0,
- maximum=50,
- step=1,
- label=i18n("保存频率save_every_epoch"),
- value=5,
- interactive=True,
- )
- total_epoch11 = gr.Slider(
- minimum=0,
- maximum=1000,
- step=1,
- label=i18n("总训练轮数total_epoch"),
- value=20,
- interactive=True,
- )
- batch_size12 = gr.Slider(
- minimum=0,
- maximum=32,
- step=1,
- label="每张显卡的batch_size",
- value=4,
- 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=["是", "否"],
- value="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=["是", "否"],
- value="是",
- interactive=True,
- )
+ save_epoch10 = gr.Slider(minimum=0, maximum=50, step=1, label='保存频率save_every_epoch', value=5,interactive=True)
+ total_epoch11 = gr.Slider(minimum=0, maximum=100, step=1, label='总训练轮数total_epoch', value=10,interactive=True)
+ batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='batch_size', value=4,interactive=True)
+ if_save_latest13 = gr.Radio(label="是否仅保存最新的ckpt文件以节省硬盘空间", choices=["是", "否"], value="否", interactive=True)
+ if_cache_gpu17 = gr.Radio(label="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=["是", "否"], value="否", interactive=True)
with gr.Row():
- pretrained_G14 = gr.Textbox(
- label=i18n("加载预训练底模G路径"),
- value="pretrained/f0G40k.pth",
- interactive=True,
- )
- pretrained_D15 = gr.Textbox(
- label=i18n("加载预训练底模D路径"),
- value="pretrained/f0D40k.pth",
- interactive=True,
- )
- sr2.change(
- change_sr2, [sr2, if_f0_3], [pretrained_G14, pretrained_D15]
- )
- if_f0_3.change(
- change_f0,
- [if_f0_3, sr2],
- [np7, 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,
- ],
- info3,
- )
- but4.click(train_index, [exp_dir1], info3)
- 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,
- )
+ pretrained_G14 = gr.Textbox(label="加载预训练底模G路径", value="pretrained/f0G40k.pth",interactive=True)
+ pretrained_D15 = gr.Textbox(label="加载预训练底模D路径", value="pretrained/f0D40k.pth",interactive=True)
+ sr2.change(change_sr2, [sr2,if_f0_3], [pretrained_G14,pretrained_D15])
+ if_f0_3.change(change_f0, [if_f0_3, sr2], [np7, f0method8, pretrained_G14, pretrained_D15])
+ gpus16 = gr.Textbox(label="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("训练模型", variant="primary")
+ but4 = gr.Button("训练特征索引", variant="primary")
+ but5 = gr.Button("一键训练", variant="primary")
+ info3 = gr.Textbox(label="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],info3)
+ but4.click(train_index,[exp_dir1],info3)
+ 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)
- with gr.TabItem(i18n("ckpt处理")):
+ with gr.TabItem("ckpt处理"):
with gr.Group():
- gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
+ gr.Markdown(value="""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,
- )
+ ckpt_a = gr.Textbox(label="A模型路径", value="", interactive=True)
+ ckpt_b = gr.Textbox(label="B模型路径", value="", interactive=True)
+ alpha_a = gr.Slider(minimum=0, maximum=1, label='A模型权重', value=0.5, interactive=True)
with gr.Row():
- 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"),
- choices=["是", "否"],
- value="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,
- )
+ sr_ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+ if_f0_ = gr.Radio(label="模型是否带音高指导", choices=["是","否"],value="是", interactive=True)
+ info__ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
+ name_to_save0=gr.Textbox(label="保存的模型名不带后缀", value="", max_lines=1, 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],
- info4,
- ) # def merge(path1,path2,alpha1,sr,f0,info):
+ but6 = gr.Button("融合", variant="primary")
+ info4 = gr.Textbox(label="输出信息", value="", max_lines=8)
+ but6.click(merge, [ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0], info4)#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)"))
+ gr.Markdown(value="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,
- )
+ ckpt_path0 = gr.Textbox(label="模型路径", value="", interactive=True)
+ info_=gr.Textbox(label="要改的模型信息", value="", max_lines=8, interactive=True)
+ name_to_save1=gr.Textbox(label="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)
+ but7 = gr.Button("修改", variant="primary")
+ info5 = gr.Textbox(label="输出信息", value="", max_lines=8)
+ but7.click(change_info, [ckpt_path0,info_,name_to_save1], info5)
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)"))
+ gr.Markdown(value="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)
+ ckpt_path1 = gr.Textbox(label="模型路径", value="", interactive=True)
+ but8 = gr.Button("查看", variant="primary")
+ info6 = gr.Textbox(label="输出信息", value="", max_lines=8)
but8.click(show_info, [ckpt_path1], info6)
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"
- )
- )
+ gr.Markdown(value="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,
- )
- 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__])
- but9.click(
- extract_small_model,
- [ckpt_path2, save_name, sr__, if_f0__, info___],
- info7,
- )
+ ckpt_path2 = gr.Textbox(label="模型路径", value="E:\codes\py39\logs\mi-test_f0_48k\\G_23333.pth", interactive=True)
+ save_name = gr.Textbox(label="保存名", value="", interactive=True)
+ sr__ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
+ if_f0__ = gr.Radio(label="模型是否带音高指导,1是0否", choices=["1","0"],value="1", interactive=True)
+ info___ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
+ but9 = gr.Button("提取", variant="primary")
+ info7 = gr.Textbox(label="输出信息", value="", max_lines=8)
+ ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
+ but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7)
- # with gr.TabItem(i18n("招募音高曲线前端编辑器")):
- # gr.Markdown(value=i18n("加开发群联系我xxxxx"))
- # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
- # gr.Markdown(value=i18n("xxxxx"))
+ with gr.TabItem("招募音高曲线前端编辑器"):
+ gr.Markdown(value="""加开发群联系我xxxxx""")
+ with gr.TabItem("点击查看交流、问题反馈群号"):
+ gr.Markdown(value="""xxxxx""")
if 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 noautoopen,
- server_port=listen_port,
- quiet=True,
- )
+ app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=listen_port,quiet=True)