Upload infer-web.py
Browse files- infer-web.py +631 -0
infer-web.py
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@@ -0,0 +1,631 @@
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1 |
+
from multiprocessing import cpu_count
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2 |
+
import threading
|
3 |
+
from time import sleep
|
4 |
+
from subprocess import Popen,PIPE,run as runn
|
5 |
+
from time import sleep
|
6 |
+
import torch, pdb, os,traceback,sys,warnings,shutil,numpy as np,faiss
|
7 |
+
#判断是否有能用来训练和加速推理的N卡
|
8 |
+
ncpu=cpu_count()
|
9 |
+
ngpu=torch.cuda.device_count()
|
10 |
+
gpu_infos=[]
|
11 |
+
if(torch.cuda.is_available()==False or ngpu==0):if_gpu_ok=False
|
12 |
+
else:
|
13 |
+
if_gpu_ok = False
|
14 |
+
for i in range(ngpu):
|
15 |
+
gpu_name=torch.cuda.get_device_name(i)
|
16 |
+
if("16"in gpu_name or "MX"in gpu_name):continue
|
17 |
+
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
|
18 |
+
if_gpu_ok=True#至少有一张能用的N卡
|
19 |
+
gpu_infos.append("%s\t%s"%(i,gpu_name))
|
20 |
+
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"
|
21 |
+
gpus="-".join([i[0]for i in gpu_infos])
|
22 |
+
now_dir=os.getcwd()
|
23 |
+
sys.path.append(now_dir)
|
24 |
+
tmp=os.path.join(now_dir,"TEMP")
|
25 |
+
shutil.rmtree(tmp,ignore_errors=True)
|
26 |
+
os.makedirs(tmp,exist_ok=True)
|
27 |
+
os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
|
28 |
+
os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
|
29 |
+
os.environ["TEMP"]=tmp
|
30 |
+
warnings.filterwarnings("ignore")
|
31 |
+
torch.manual_seed(114514)
|
32 |
+
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
|
33 |
+
from scipy.io import wavfile
|
34 |
+
from fairseq import checkpoint_utils
|
35 |
+
import gradio as gr
|
36 |
+
import librosa
|
37 |
+
import logging
|
38 |
+
from vc_infer_pipeline import VC
|
39 |
+
import soundfile as sf
|
40 |
+
from config import is_half,device,is_half
|
41 |
+
from infer_uvr5 import _audio_pre_
|
42 |
+
from my_utils import load_audio
|
43 |
+
from train.process_ckpt import show_info,change_info,merge,extract_small_model
|
44 |
+
# from trainset_preprocess_pipeline import PreProcess
|
45 |
+
logging.getLogger('numba').setLevel(logging.WARNING)
|
46 |
+
|
47 |
+
class ToolButton(gr.Button, gr.components.FormComponent):
|
48 |
+
"""Small button with single emoji as text, fits inside gradio forms"""
|
49 |
+
def __init__(self, **kwargs):
|
50 |
+
super().__init__(variant="tool", **kwargs)
|
51 |
+
def get_block_name(self):
|
52 |
+
return "button"
|
53 |
+
|
54 |
+
hubert_model=None
|
55 |
+
def load_hubert():
|
56 |
+
global hubert_model
|
57 |
+
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
|
58 |
+
hubert_model = models[0]
|
59 |
+
hubert_model = hubert_model.to(device)
|
60 |
+
if(is_half):hubert_model = hubert_model.half()
|
61 |
+
else:hubert_model = hubert_model.float()
|
62 |
+
hubert_model.eval()
|
63 |
+
|
64 |
+
weight_root="weights"
|
65 |
+
weight_uvr5_root="uvr5_weights"
|
66 |
+
names=[]
|
67 |
+
for name in os.listdir(weight_root):names.append(name)
|
68 |
+
uvr5_names=[]
|
69 |
+
for name in os.listdir(weight_uvr5_root):uvr5_names.append(name.replace(".pth",""))
|
70 |
+
|
71 |
+
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
|
72 |
+
global tgt_sr,net_g,vc,hubert_model
|
73 |
+
if input_audio is None:return "You need to upload an audio", None
|
74 |
+
f0_up_key = int(f0_up_key)
|
75 |
+
try:
|
76 |
+
audio=load_audio(input_audio,16000)
|
77 |
+
times = [0, 0, 0]
|
78 |
+
if(hubert_model==None):load_hubert()
|
79 |
+
if_f0 = cpt.get("f0", 1)
|
80 |
+
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)
|
81 |
+
print(times)
|
82 |
+
return "Success", (tgt_sr, audio_opt)
|
83 |
+
except:
|
84 |
+
info=traceback.format_exc()
|
85 |
+
print(info)
|
86 |
+
return info,(None,None)
|
87 |
+
|
88 |
+
def vc_multi(sid,dir_path,opt_root,paths,f0_up_key,f0_method,file_index,file_big_npy,index_rate):
|
89 |
+
try:
|
90 |
+
dir_path=dir_path.strip(" ")#防止小白拷路径头尾带了空格
|
91 |
+
opt_root=opt_root.strip(" ")
|
92 |
+
os.makedirs(opt_root, exist_ok=True)
|
93 |
+
try:
|
94 |
+
if(dir_path!=""):paths=[os.path.join(dir_path,name)for name in os.listdir(dir_path)]
|
95 |
+
else:paths=[path.name for path in paths]
|
96 |
+
except:
|
97 |
+
traceback.print_exc()
|
98 |
+
paths = [path.name for path in paths]
|
99 |
+
infos=[]
|
100 |
+
for path in paths:
|
101 |
+
info,opt=vc_single(sid,path,f0_up_key,None,f0_method,file_index,file_big_npy,index_rate)
|
102 |
+
if(info=="Success"):
|
103 |
+
try:
|
104 |
+
tgt_sr,audio_opt=opt
|
105 |
+
wavfile.write("%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt)
|
106 |
+
except:
|
107 |
+
info=traceback.format_exc()
|
108 |
+
infos.append("%s->%s"%(os.path.basename(path),info))
|
109 |
+
yield "\n".join(infos)
|
110 |
+
yield "\n".join(infos)
|
111 |
+
except:
|
112 |
+
yield traceback.format_exc()
|
113 |
+
|
114 |
+
def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins):
|
115 |
+
infos = []
|
116 |
+
try:
|
117 |
+
inp_root = inp_root.strip(" ").strip("\n")
|
118 |
+
save_root_vocal = save_root_vocal.strip(" ").strip("\n")
|
119 |
+
save_root_ins = save_root_ins.strip(" ").strip("\n")
|
120 |
+
pre_fun = _audio_pre_(model_path=os.path.join(weight_uvr5_root,model_name+".pth"), device=device, is_half=is_half)
|
121 |
+
if (inp_root != ""):paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
|
122 |
+
else:paths = [path.name for path in paths]
|
123 |
+
for name in paths:
|
124 |
+
inp_path=os.path.join(inp_root,name)
|
125 |
+
try:
|
126 |
+
pre_fun._path_audio_(inp_path , save_root_ins,save_root_vocal)
|
127 |
+
infos.append("%s->Success"%(os.path.basename(inp_path)))
|
128 |
+
yield "\n".join(infos)
|
129 |
+
except:
|
130 |
+
infos.append("%s->%s" % (os.path.basename(inp_path),traceback.format_exc()))
|
131 |
+
yield "\n".join(infos)
|
132 |
+
except:
|
133 |
+
infos.append(traceback.format_exc())
|
134 |
+
yield "\n".join(infos)
|
135 |
+
finally:
|
136 |
+
try:
|
137 |
+
del pre_fun.model
|
138 |
+
del pre_fun
|
139 |
+
except:
|
140 |
+
traceback.print_exc()
|
141 |
+
print("clean_empty_cache")
|
142 |
+
torch.cuda.empty_cache()
|
143 |
+
yield "\n".join(infos)
|
144 |
+
|
145 |
+
#一个选项卡全局只能有一个音色
|
146 |
+
def get_vc(sid):
|
147 |
+
global n_spk,tgt_sr,net_g,vc,cpt
|
148 |
+
if(sid==""):
|
149 |
+
global hubert_model
|
150 |
+
print("clean_empty_cache")
|
151 |
+
del net_g, n_spk, vc, hubert_model,tgt_sr#,cpt
|
152 |
+
hubert_model = net_g=n_spk=vc=hubert_model=tgt_sr=None
|
153 |
+
torch.cuda.empty_cache()
|
154 |
+
###楼下不这么折腾清理不干净
|
155 |
+
if_f0 = cpt.get("f0", 1)
|
156 |
+
if (if_f0 == 1):
|
157 |
+
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
|
158 |
+
else:
|
159 |
+
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
160 |
+
del net_g,cpt
|
161 |
+
torch.cuda.empty_cache()
|
162 |
+
cpt=None
|
163 |
+
return {"visible": False, "__type__": "update"}
|
164 |
+
person = "%s/%s" % (weight_root, sid)
|
165 |
+
print("loading %s"%person)
|
166 |
+
cpt = torch.load(person, map_location="cpu")
|
167 |
+
tgt_sr = cpt["config"][-1]
|
168 |
+
cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
|
169 |
+
if_f0=cpt.get("f0",1)
|
170 |
+
if(if_f0==1):
|
171 |
+
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
|
172 |
+
else:
|
173 |
+
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
174 |
+
del net_g.enc_q
|
175 |
+
print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净,真奇葩
|
176 |
+
net_g.eval().to(device)
|
177 |
+
if (is_half):net_g = net_g.half()
|
178 |
+
else:net_g = net_g.float()
|
179 |
+
vc = VC(tgt_sr, device, is_half)
|
180 |
+
n_spk=cpt["config"][-3]
|
181 |
+
return {"visible": True,"maximum": n_spk, "__type__": "update"}
|
182 |
+
|
183 |
+
def change_choices():return {"choices": sorted(list(os.listdir(weight_root))), "__type__": "update"}
|
184 |
+
def clean():return {"value": "", "__type__": "update"}
|
185 |
+
def change_f0(if_f0_3,sr2):#np7, f0method8,pretrained_G14,pretrained_D15
|
186 |
+
if(if_f0_3=="是"):return {"visible": True, "__type__": "update"},{"visible": True, "__type__": "update"},"pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2
|
187 |
+
return {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"},"pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2
|
188 |
+
|
189 |
+
sr_dict={
|
190 |
+
"32k":32000,
|
191 |
+
"40k":40000,
|
192 |
+
"48k":48000,
|
193 |
+
}
|
194 |
+
|
195 |
+
def if_done(done,p):
|
196 |
+
while 1:
|
197 |
+
if(p.poll()==None):sleep(0.5)
|
198 |
+
else:break
|
199 |
+
done[0]=True
|
200 |
+
|
201 |
+
|
202 |
+
def if_done_multi(done,ps):
|
203 |
+
while 1:
|
204 |
+
#poll==None代表进程未结束
|
205 |
+
#只要有一个进程未结束都不停
|
206 |
+
flag=1
|
207 |
+
for p in ps:
|
208 |
+
if(p.poll()==None):
|
209 |
+
flag = 0
|
210 |
+
sleep(0.5)
|
211 |
+
break
|
212 |
+
if(flag==1):break
|
213 |
+
done[0]=True
|
214 |
+
|
215 |
+
def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu):
|
216 |
+
sr=sr_dict[sr]
|
217 |
+
os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
|
218 |
+
f = open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "w")
|
219 |
+
f.close()
|
220 |
+
cmd="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir,sr,n_p,now_dir,exp_dir)
|
221 |
+
print(cmd)
|
222 |
+
p = Popen(cmd, shell=True)#, stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
|
223 |
+
###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
|
224 |
+
done=[False]
|
225 |
+
threading.Thread(target=if_done,args=(done,p,)).start()
|
226 |
+
while(1):
|
227 |
+
with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir),"r")as f:yield(f.read())
|
228 |
+
sleep(1)
|
229 |
+
if(done[0]==True):break
|
230 |
+
with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "r")as f:log = f.read()
|
231 |
+
print(log)
|
232 |
+
yield log
|
233 |
+
#but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
|
234 |
+
def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
|
235 |
+
gpus=gpus.split("-")#
|
236 |
+
os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
|
237 |
+
f = open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "w")
|
238 |
+
f.close()
|
239 |
+
if(if_f0=="是"):
|
240 |
+
cmd="runtime\python.exe extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
|
241 |
+
print(cmd)
|
242 |
+
p = Popen(cmd, shell=True,cwd=now_dir)#, stdin=PIPE, stdout=PIPE,stderr=PIPE
|
243 |
+
###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
|
244 |
+
done=[False]
|
245 |
+
threading.Thread(target=if_done,args=(done,p,)).start()
|
246 |
+
while(1):
|
247 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir),"r")as f:yield(f.read())
|
248 |
+
sleep(1)
|
249 |
+
if(done[0]==True):break
|
250 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read()
|
251 |
+
print(log)
|
252 |
+
yield log
|
253 |
+
####对不同part分别开多进程
|
254 |
+
'''
|
255 |
+
n_part=int(sys.argv[1])
|
256 |
+
i_part=int(sys.argv[2])
|
257 |
+
i_gpu=sys.argv[3]
|
258 |
+
exp_dir=sys.argv[4]
|
259 |
+
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
|
260 |
+
'''
|
261 |
+
leng=len(gpus)
|
262 |
+
ps=[]
|
263 |
+
for idx,n_g in enumerate(gpus):
|
264 |
+
cmd="runtime\python.exe extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
|
265 |
+
print(cmd)
|
266 |
+
p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
267 |
+
ps.append(p)
|
268 |
+
###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
|
269 |
+
done = [False]
|
270 |
+
threading.Thread(target=if_done_multi, args=(done, ps,)).start()
|
271 |
+
while (1):
|
272 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:yield (f.read())
|
273 |
+
sleep(1)
|
274 |
+
if (done[0] == True): break
|
275 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read()
|
276 |
+
print(log)
|
277 |
+
yield log
|
278 |
+
def change_sr2(sr2,if_f0_3):
|
279 |
+
if(if_f0_3=="是"):return "pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2
|
280 |
+
else:return "pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2
|
281 |
+
#but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
|
282 |
+
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):
|
283 |
+
#生成filelist
|
284 |
+
exp_dir="%s/logs/%s"%(now_dir,exp_dir1)
|
285 |
+
os.makedirs(exp_dir,exist_ok=True)
|
286 |
+
gt_wavs_dir="%s/0_gt_wavs"%(exp_dir)
|
287 |
+
co256_dir="%s/3_feature256"%(exp_dir)
|
288 |
+
if(if_f0_3=="是"):
|
289 |
+
f0_dir = "%s/2a_f0" % (exp_dir)
|
290 |
+
f0nsf_dir="%s/2b-f0nsf"%(exp_dir)
|
291 |
+
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)])
|
292 |
+
else:
|
293 |
+
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])
|
294 |
+
opt=[]
|
295 |
+
for name in names:
|
296 |
+
if (if_f0_3 == "是"):
|
297 |
+
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))
|
298 |
+
else:
|
299 |
+
opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
|
300 |
+
with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
|
301 |
+
print("write filelist done")
|
302 |
+
#生成config#无需生成config
|
303 |
+
# cmd = "runtime\python.exe 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"
|
304 |
+
cmd = "runtime\python.exe 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)
|
305 |
+
print(cmd)
|
306 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
307 |
+
p.wait()
|
308 |
+
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"
|
309 |
+
# but4.click(train_index, [exp_dir1], info3)
|
310 |
+
def train_index(exp_dir1):
|
311 |
+
exp_dir="%s/logs/%s"%(now_dir,exp_dir1)
|
312 |
+
os.makedirs(exp_dir,exist_ok=True)
|
313 |
+
feature_dir="%s/3_feature256"%(exp_dir)
|
314 |
+
if(os.path.exists(feature_dir)==False):return "请先进行特征提取!"
|
315 |
+
listdir_res=list(os.listdir(feature_dir))
|
316 |
+
if(len(listdir_res)==0):return "请先进行特征提取!"
|
317 |
+
npys = []
|
318 |
+
for name in sorted(listdir_res):
|
319 |
+
phone = np.load("%s/%s" % (feature_dir, name))
|
320 |
+
npys.append(phone)
|
321 |
+
big_npy = np.concatenate(npys, 0)
|
322 |
+
np.save("%s/total_fea.npy"%exp_dir, big_npy)
|
323 |
+
n_ivf = big_npy.shape[0] // 39
|
324 |
+
infos=[]
|
325 |
+
infos.append("%s,%s"%(big_npy.shape,n_ivf))
|
326 |
+
yield "\n".join(infos)
|
327 |
+
index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf)
|
328 |
+
infos.append("training")
|
329 |
+
yield "\n".join(infos)
|
330 |
+
index_ivf = faiss.extract_index_ivf(index) #
|
331 |
+
index_ivf.nprobe = int(np.power(n_ivf,0.3))
|
332 |
+
index.train(big_npy)
|
333 |
+
faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
|
334 |
+
infos.append("adding")
|
335 |
+
yield "\n".join(infos)
|
336 |
+
index.add(big_npy)
|
337 |
+
faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
|
338 |
+
infos.append("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe))
|
339 |
+
yield "\n".join(infos)
|
340 |
+
#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)
|
341 |
+
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):
|
342 |
+
infos=[]
|
343 |
+
def get_info_str(strr):
|
344 |
+
infos.append(strr)
|
345 |
+
return "\n".join(infos)
|
346 |
+
os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True)
|
347 |
+
#########step1:处理数据
|
348 |
+
open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close()
|
349 |
+
cmd="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)
|
350 |
+
yield get_info_str("step1:正在处理数据")
|
351 |
+
yield get_info_str(cmd)
|
352 |
+
p = Popen(cmd, shell=True)
|
353 |
+
p.wait()
|
354 |
+
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r")as f: print(f.read())
|
355 |
+
#########step2a:提取音高
|
356 |
+
open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
|
357 |
+
if(if_f0_3=="是"):
|
358 |
+
yield get_info_str("step2a:正在提取音高")
|
359 |
+
cmd="runtime\python.exe extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
|
360 |
+
yield get_info_str(cmd)
|
361 |
+
p = Popen(cmd, shell=True,cwd=now_dir)
|
362 |
+
p.wait()
|
363 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read())
|
364 |
+
else:yield get_info_str("step2a:无需提取音高")
|
365 |
+
#######step2b:提取特征
|
366 |
+
yield get_info_str("step2b:正在提取特征")
|
367 |
+
gpus=gpus16.split("-")
|
368 |
+
leng=len(gpus)
|
369 |
+
ps=[]
|
370 |
+
for idx,n_g in enumerate(gpus):
|
371 |
+
cmd="runtime\python.exe extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
|
372 |
+
yield get_info_str(cmd)
|
373 |
+
p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
|
374 |
+
ps.append(p)
|
375 |
+
for p in ps:p.wait()
|
376 |
+
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read())
|
377 |
+
#######step3a:训练模型
|
378 |
+
yield get_info_str("step3a:正在训练模型")
|
379 |
+
#生成filelist
|
380 |
+
exp_dir="%s/logs/%s"%(now_dir,exp_dir1)
|
381 |
+
gt_wavs_dir="%s/0_gt_wavs"%(exp_dir)
|
382 |
+
co256_dir="%s/3_feature256"%(exp_dir)
|
383 |
+
if(if_f0_3=="是"):
|
384 |
+
f0_dir = "%s/2a_f0" % (exp_dir)
|
385 |
+
f0nsf_dir="%s/2b-f0nsf"%(exp_dir)
|
386 |
+
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)])
|
387 |
+
else:
|
388 |
+
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])
|
389 |
+
opt=[]
|
390 |
+
for name in names:
|
391 |
+
if (if_f0_3 == "是"):
|
392 |
+
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))
|
393 |
+
else:
|
394 |
+
opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
|
395 |
+
with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
|
396 |
+
yield get_info_str("write filelist done")
|
397 |
+
cmd = "runtime\python.exe 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)
|
398 |
+
yield get_info_str(cmd)
|
399 |
+
p = Popen(cmd, shell=True, cwd=now_dir)
|
400 |
+
p.wait()
|
401 |
+
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")
|
402 |
+
#######step3b:训练索引
|
403 |
+
feature_dir="%s/3_feature256"%(exp_dir)
|
404 |
+
npys = []
|
405 |
+
listdir_res=list(os.listdir(feature_dir))
|
406 |
+
for name in sorted(listdir_res):
|
407 |
+
phone = np.load("%s/%s" % (feature_dir, name))
|
408 |
+
npys.append(phone)
|
409 |
+
big_npy = np.concatenate(npys, 0)
|
410 |
+
np.save("%s/total_fea.npy"%exp_dir, big_npy)
|
411 |
+
n_ivf = big_npy.shape[0] // 39
|
412 |
+
yield get_info_str("%s,%s"%(big_npy.shape,n_ivf))
|
413 |
+
index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf)
|
414 |
+
yield get_info_str("training index")
|
415 |
+
index_ivf = faiss.extract_index_ivf(index) #
|
416 |
+
index_ivf.nprobe = int(np.power(n_ivf,0.3))
|
417 |
+
index.train(big_npy)
|
418 |
+
faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
|
419 |
+
yield get_info_str("adding index")
|
420 |
+
index.add(big_npy)
|
421 |
+
faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
|
422 |
+
yield get_info_str("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe))
|
423 |
+
yield get_info_str("全流程结束!")
|
424 |
+
|
425 |
+
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
426 |
+
def change_info_(ckpt_path):
|
427 |
+
if(os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"))==False):return {"__type__": "update"},{"__type__": "update"}
|
428 |
+
try:
|
429 |
+
with open(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"),"r")as f:
|
430 |
+
info=eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
|
431 |
+
sr,f0=info["sample_rate"],info["if_f0"]
|
432 |
+
return sr,str(f0)
|
433 |
+
except:
|
434 |
+
traceback.print_exc()
|
435 |
+
return {"__type__": "update"}, {"__type__": "update"}
|
436 |
+
|
437 |
+
|
438 |
+
with gr.Blocks() as app:
|
439 |
+
gr.Markdown(value="""
|
440 |
+
本软件以MIT协议开源,作者不对软件具备任何控制力,使用软件者、传播软件导出的声音者自负全责。<br>
|
441 |
+
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"。
|
442 |
+
""")
|
443 |
+
with gr.Tabs():
|
444 |
+
with gr.TabItem("模型推理"):
|
445 |
+
with gr.Row():
|
446 |
+
sid0 = gr.Dropdown(label="推理音色", choices=names)
|
447 |
+
refresh_button = gr.Button("刷新音色列表", variant="primary")
|
448 |
+
refresh_button.click(
|
449 |
+
fn=change_choices,
|
450 |
+
inputs=[],
|
451 |
+
outputs=[sid0]
|
452 |
+
)
|
453 |
+
clean_button = gr.Button("卸载音色省显存", variant="primary")
|
454 |
+
spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label='请选择说话人id', value=0, visible=False, interactive=True)
|
455 |
+
clean_button.click(
|
456 |
+
fn=clean,
|
457 |
+
inputs=[],
|
458 |
+
outputs=[sid0]
|
459 |
+
)
|
460 |
+
sid0.change(
|
461 |
+
fn=get_vc,
|
462 |
+
inputs=[sid0],
|
463 |
+
outputs=[spk_item],
|
464 |
+
)
|
465 |
+
with gr.Group():
|
466 |
+
gr.Markdown(value="""
|
467 |
+
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。
|
468 |
+
""")
|
469 |
+
with gr.Row():
|
470 |
+
with gr.Column():
|
471 |
+
vc_transform0 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=0)
|
472 |
+
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")
|
473 |
+
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)
|
474 |
+
with gr.Column():
|
475 |
+
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)
|
476 |
+
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)
|
477 |
+
index_rate1 = gr.Slider(minimum=0, maximum=1,label='检索特征占比', value=1,interactive=True)
|
478 |
+
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")
|
479 |
+
but0=gr.Button("转换", variant="primary")
|
480 |
+
with gr.Column():
|
481 |
+
vc_output1 = gr.Textbox(label="输出信息")
|
482 |
+
vc_output2 = gr.Audio(label="输出音频(右下角三个点,点了可以下载)")
|
483 |
+
but0.click(vc_single, [spk_item, input_audio0, vc_transform0,f0_file,f0method0,file_index1,file_big_npy1,index_rate1], [vc_output1, vc_output2])
|
484 |
+
with gr.Group():
|
485 |
+
gr.Markdown(value="""
|
486 |
+
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。
|
487 |
+
""")
|
488 |
+
with gr.Row():
|
489 |
+
with gr.Column():
|
490 |
+
vc_transform1 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=0)
|
491 |
+
opt_input = gr.Textbox(label="指定输出文件夹",value="opt")
|
492 |
+
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)
|
493 |
+
with gr.Column():
|
494 |
+
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)
|
495 |
+
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)
|
496 |
+
index_rate2 = gr.Slider(minimum=0, maximum=1,label='检索特征占比', value=1,interactive=True)
|
497 |
+
with gr.Column():
|
498 |
+
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")
|
499 |
+
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")
|
500 |
+
but1=gr.Button("转换", variant="primary")
|
501 |
+
vc_output3 = gr.Textbox(label="输出信息")
|
502 |
+
but1.click(vc_multi, [spk_item, dir_input,opt_input,inputs, vc_transform1,f0method1,file_index2,file_big_npy2,index_rate2], [vc_output3])
|
503 |
+
with gr.TabItem("伴奏人声分离"):
|
504 |
+
with gr.Group():
|
505 |
+
gr.Markdown(value="""
|
506 |
+
人声伴奏分离批量处理,使用UVR5模型。<br>
|
507 |
+
不带和声用HP2,带和声且提取的人声不需要和声用HP5<br>
|
508 |
+
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)
|
509 |
+
""")
|
510 |
+
with gr.Row():
|
511 |
+
with gr.Column():
|
512 |
+
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")
|
513 |
+
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")
|
514 |
+
with gr.Column():
|
515 |
+
model_choose = gr.Dropdown(label="模型", choices=uvr5_names)
|
516 |
+
opt_vocal_root = gr.Textbox(label="指定输出人声文件夹",value="opt")
|
517 |
+
opt_ins_root = gr.Textbox(label="指定输出乐器文件夹",value="opt")
|
518 |
+
but2=gr.Button("转换", variant="primary")
|
519 |
+
vc_output4 = gr.Textbox(label="输出信息")
|
520 |
+
but2.click(uvr, [model_choose, dir_wav_input,opt_vocal_root,wav_inputs,opt_ins_root], [vc_output4])
|
521 |
+
with gr.TabItem("训练"):
|
522 |
+
gr.Markdown(value="""
|
523 |
+
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。
|
524 |
+
""")
|
525 |
+
with gr.Row():
|
526 |
+
exp_dir1 = gr.Textbox(label="输入实验名",value="xxxx")
|
527 |
+
sr2 = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
|
528 |
+
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)
|
529 |
+
with gr.Group():#暂时单人的,后面支持最多4人的#数据处理
|
530 |
+
gr.Markdown(value="""
|
531 |
+
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。
|
532 |
+
""")
|
533 |
+
with gr.Row():
|
534 |
+
trainset_dir4 = gr.Textbox(label="输入训练文件夹路径",value="E:\\xxx\\xxxxx")
|
535 |
+
spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label='请指定说话人id', value=0,interactive=True)
|
536 |
+
but1=gr.Button("处理数据", variant="primary")
|
537 |
+
info1=gr.Textbox(label="输出信息",value="")
|
538 |
+
but1.click(preprocess_dataset,[trainset_dir4,exp_dir1,sr2],[info1])
|
539 |
+
with gr.Group():
|
540 |
+
gr.Markdown(value="""
|
541 |
+
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)
|
542 |
+
""")
|
543 |
+
with gr.Row():
|
544 |
+
with gr.Column():
|
545 |
+
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)
|
546 |
+
gpu_info9 = gr.Textbox(label="显卡信息",value=gpu_info)
|
547 |
+
with gr.Column():
|
548 |
+
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)
|
549 |
+
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)
|
550 |
+
but2=gr.Button("特征提取", variant="primary")
|
551 |
+
info2=gr.Textbox(label="���出信息",value="",max_lines=8)
|
552 |
+
but2.click(extract_f0_feature,[gpus6,np7,f0method8,if_f0_3,exp_dir1],[info2])
|
553 |
+
with gr.Group():
|
554 |
+
gr.Markdown(value="""
|
555 |
+
step3:填写训练设置,开始训练模型和索引
|
556 |
+
""")
|
557 |
+
with gr.Row():
|
558 |
+
save_epoch10 = gr.Slider(minimum=0, maximum=50, step=1, label='保存频率save_every_epoch', value=5,interactive=True)
|
559 |
+
total_epoch11 = gr.Slider(minimum=0, maximum=100, step=1, label='总训练轮数total_epoch', value=10,interactive=True)
|
560 |
+
batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='batch_size', value=4,interactive=True)
|
561 |
+
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)
|
562 |
+
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)
|
563 |
+
with gr.Row():
|
564 |
+
pretrained_G14 = gr.Textbox(label="加载预训练底模G路径", value="pretrained/f0G40k.pth",interactive=True)
|
565 |
+
pretrained_D15 = gr.Textbox(label="加载预训练底模D路径", value="pretrained/f0D40k.pth",interactive=True)
|
566 |
+
sr2.change(change_sr2, [sr2,if_f0_3], [pretrained_G14,pretrained_D15])
|
567 |
+
if_f0_3.change(change_f0, [if_f0_3, sr2], [np7, f0method8, pretrained_G14, pretrained_D15])
|
568 |
+
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)
|
569 |
+
but3 = gr.Button("训练模型", variant="primary")
|
570 |
+
but4 = gr.Button("训练特征索引", variant="primary")
|
571 |
+
but5 = gr.Button("一键训练", variant="primary")
|
572 |
+
info3 = gr.Textbox(label="输出信息", value="",max_lines=10)
|
573 |
+
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)
|
574 |
+
but4.click(train_index,[exp_dir1],info3)
|
575 |
+
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)
|
576 |
+
|
577 |
+
with gr.TabItem("ckpt处理"):
|
578 |
+
with gr.Group():
|
579 |
+
gr.Markdown(value="""模型融合,可用于测试音色融合""")
|
580 |
+
with gr.Row():
|
581 |
+
ckpt_a = gr.Textbox(label="A模型路径", value="", interactive=True)
|
582 |
+
ckpt_b = gr.Textbox(label="B模型路径", value="", interactive=True)
|
583 |
+
alpha_a = gr.Slider(minimum=0, maximum=1, label='A模型权重', value=0.5, interactive=True)
|
584 |
+
with gr.Row():
|
585 |
+
sr_ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
|
586 |
+
if_f0_ = gr.Radio(label="模型是否带音高指导", choices=["是","否"],value="是", interactive=True)
|
587 |
+
info__ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
|
588 |
+
name_to_save0=gr.Textbox(label="保存的模型名不带后缀", value="", max_lines=1, interactive=True)
|
589 |
+
with gr.Row():
|
590 |
+
but6 = gr.Button("融合", variant="primary")
|
591 |
+
info4 = gr.Textbox(label="输出信息", value="", max_lines=8)
|
592 |
+
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):
|
593 |
+
with gr.Group():
|
594 |
+
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)")
|
595 |
+
with gr.Row():
|
596 |
+
ckpt_path0 = gr.Textbox(label="模型路径", value="", interactive=True)
|
597 |
+
info_=gr.Textbox(label="要改的模型信息", value="", max_lines=8, interactive=True)
|
598 |
+
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)
|
599 |
+
with gr.Row():
|
600 |
+
but7 = gr.Button("修改", variant="primary")
|
601 |
+
info5 = gr.Textbox(label="输出信息", value="", max_lines=8)
|
602 |
+
but7.click(change_info, [ckpt_path0,info_,name_to_save1], info5)
|
603 |
+
with gr.Group():
|
604 |
+
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)")
|
605 |
+
with gr.Row():
|
606 |
+
ckpt_path1 = gr.Textbox(label="模型路径", value="", interactive=True)
|
607 |
+
but8 = gr.Button("查看", variant="primary")
|
608 |
+
info6 = gr.Textbox(label="���出信息", value="", max_lines=8)
|
609 |
+
but8.click(show_info, [ckpt_path1], info6)
|
610 |
+
with gr.Group():
|
611 |
+
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")
|
612 |
+
with gr.Row():
|
613 |
+
ckpt_path2 = gr.Textbox(label="模型路径", value="E:\codes\py39\logs\mi-test_f0_48k\\G_23333.pth", interactive=True)
|
614 |
+
save_name = gr.Textbox(label="保存名", value="", interactive=True)
|
615 |
+
sr__ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True)
|
616 |
+
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)
|
617 |
+
info___ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True)
|
618 |
+
but9 = gr.Button("提取", variant="primary")
|
619 |
+
info7 = gr.Textbox(label="输出信息", value="", max_lines=8)
|
620 |
+
ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
621 |
+
but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7)
|
622 |
+
|
623 |
+
with gr.TabItem("招募音高曲线前端编辑器"):
|
624 |
+
gr.Markdown(value="""加开发群联系我647947694""")
|
625 |
+
with gr.TabItem("招募实时变声插件开发"):
|
626 |
+
gr.Markdown(value="""加开发群联系我647947694""")
|
627 |
+
with gr.TabItem("点击查看交流、问题反馈群号"):
|
628 |
+
gr.Markdown(value="""259421308""")
|
629 |
+
|
630 |
+
# app.launch(server_name="0.0.0.0",server_port=7860)
|
631 |
+
app.queue(concurrency_count=511, max_size=1022).launch(server_name="127.0.0.1",inbrowser=True,server_port=7865,quiet=True,share=True)
|