diff --git a/export_onnx.py b/export_onnx.py
index 80f061b..8b62b47 100644
--- a/export_onnx.py
+++ b/export_onnx.py
@@ -1,36 +1,44 @@
-from infer_pack.models_onnx import SynthesizerTrnMs256NSFsid
+from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
+from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
import torch
-person = "Shiroha/shiroha.pth"
-exported_path = "model.onnx"
+if __name__ == '__main__':
+ MoeVS = True #模型是否为MoeVoiceStudio（原MoeSS）使用
+ 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(ModelPath, 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）
-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)
+ device = "cpu" #导出时设备（不影响使用模型）
-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,
+ if MoeVS:
+ net_g = SynthesizerTrnMs256NSFsidM(*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.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),
),
- exported_path,
+ ExportedPath,
dynamic_axes={
"phone": [1],
"pitch": [1],
@@ -41,4 +49,33 @@ torch.onnx.export(net_g,
opset_version=16,
verbose=False,
input_names=input_names,
- output_names=output_names)
\ No newline at end of file
+ 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
diff --git a/infer-web.py b/infer-web.py
index 751bf0f..771a65c 100644
--- a/infer-web.py
+++ b/infer-web.py
@@ -3,132 +3,259 @@ 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
-#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
+
+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
else:
if_gpu_ok = False
for i in range(ngpu):
- 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)
+ 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])
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,is_half,python_cmd,listen_port,iscolab,noparallel
+from config import (
+ is_half,
+ device,
+ python_cmd,
+ listen_port,
+ iscolab,
+ noparallel,
+ noautoopen,
+)
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, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
+ models, _, _ = 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=[]
-for name in os.listdir(weight_root):
- 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",""))
-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
+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 = []
+for name in os.listdir(weight_uvr5_root):
+ 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
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)
- 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)
+ 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=""
+ )
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(" ")#防止小白拷路径头尾带了空格
- opt_root=opt_root.strip(" ")
+ 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(" ")
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("\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]
+ 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]
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())
@@ -140,505 +267,1201 @@ def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins):
except:
traceback.print_exc()
print("clean_empty_cache")
- torch.cuda.empty_cache()
+ if torch.cuda.is_available():
+ 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
- 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
+ if torch.cuda.is_available():
+ 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
- torch.cuda.empty_cache()
- cpt=None
+ del net_g, cpt
+ if torch.cuda.is_available():
+ 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
-sr_dict={
- "32k":32000,
- "40k":40000,
- "48k":48000,
+
+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,
}
-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/logs/%s"%(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 %s/logs/%s" % (
+ device,
+ 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))
- 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
- # 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)
- 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)
+ 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:
- 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)
+ 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))
+ print("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"
+ 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,
+ )
+ )
+ 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,
+ )
+ )
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/logs/%s"%(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 %s/logs/%s" % (
+ device,
+ 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))
- 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,
+ )
+ )
+ 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))
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"}
+from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
+from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
+def export_onnx(ModelPath, ExportedPath, MoeVS=True):
+ hidden_channels = 256 # hidden_channels，为768Vec做准备
+ cpt = torch.load(ModelPath, 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）
+
+ if MoeVS:
+ net_g = SynthesizerTrnMs256NSFsidM(*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.to(device),
+ test_phone_lengths.to(device),
+ test_pitch.to(device),
+ test_pitchf.to(device),
+ test_ds.to(device),
+ test_rnd.to(device),
+ ),
+ ExportedPath,
+ dynamic_axes={
+ "phone": [1],
+ "pitch": [1],
+ "pitchf": [1],
+ "rnd": [2],
+ },
+ do_constant_folding=False,
+ 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,
+ )
+ return "Finished"
+
with gr.Blocks() as app:
- 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"。
- """)
+ 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."
+ )
+ )
with gr.Tabs():
- with gr.TabItem("模型推理"):
+ with gr.TabItem(i18n("模型推理")):
with gr.Row():
- 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]
+ 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,
)
+ clean_button.click(fn=clean, inputs=[], outputs=[sid0])
sid0.change(
fn=get_vc,
inputs=[sid0],
outputs=[spk_item],
)
with gr.Group():
- 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。
- """)
+ 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="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)
+ 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,
+ )
with gr.Column():
- 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")
+ 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")
with gr.Column():
- 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])
+ 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],
+ )
with gr.Group():
- 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。
- """)
+ 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="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)
+ 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,
+ )
with gr.Column():
- 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)
+ 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,
+ )
with gr.Column():
- 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"):
+ 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")):
with gr.Group():
- 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）
- """)
+ 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)"
+ )
+ )
with gr.Row():
with gr.Column():
- 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")
+ 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")
+ )
with gr.Column():
- 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。
- """)
+ 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. "
+ )
+ )
with gr.Row():
- 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。
- """)
+ 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. "
+ )
+ )
with gr.Row():
- 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])
+ 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]
+ )
with gr.Group():
- 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)
- """)
+ 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="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)
+ 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)
with gr.Column():
- 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])
+ 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],
+ )
with gr.Group():
- gr.Markdown(value="""
- step3：填写训练设置，开始训练模型和索引
- """)
+ 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=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)
+ 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,
+ )
with gr.Row():
- 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)
+ 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,
+ )
- with gr.TabItem("ckpt处理"):
+ with gr.TabItem(i18n("ckpt处理")):
with gr.Group():
- gr.Markdown(value="""模型融合，可用于测试音色融合""")
+ 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="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)
+ 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="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)
+ 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,
+ )
with gr.Row():
- 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):
+ 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):
with gr.Group():
- 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)")
+ 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="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)
+ 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("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)
+ 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)
with gr.Group():
- 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)")
+ 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="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)
+ 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)
with gr.Group():
- 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")
+ 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="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)
+ 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,
+ )
- 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""")
+ 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():
+ moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True)
+ infoOnnx = gr.Label(label="Null")
+ with gr.Row():
+ butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
+ butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
+
+ # 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"))
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=True,server_port=listen_port,quiet=True)
+ app.queue(concurrency_count=511, max_size=1022).launch(
+ server_name="0.0.0.0",
+ inbrowser=not noautoopen,
+ server_port=listen_port,
+ quiet=True,
+ )