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import os,shutil,sys,pdb,re
now_dir = os.getcwd()
sys.path.insert(0, now_dir)
import json,yaml,warnings,torch
import platform
import psutil
import signal
warnings.filterwarnings("ignore")
torch.manual_seed(233333)
tmp = os.path.join(now_dir, "TEMP")
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
if(os.path.exists(tmp)):
for name in os.listdir(tmp):
if(name=="jieba.cache"):continue
path="%s/%s"%(tmp,name)
delete=os.remove if os.path.isfile(path) else shutil.rmtree
try:
delete(path)
except Exception as e:
print(str(e))
pass
import site
site_packages_roots = []
for path in site.getsitepackages():
if "packages" in path:
site_packages_roots.append(path)
if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" % now_dir]
#os.environ["OPENBLAS_NUM_THREADS"] = "4"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
os.environ["all_proxy"] = ""
for site_packages_root in site_packages_roots:
if os.path.exists(site_packages_root):
try:
with open("%s/users.pth" % (site_packages_root), "w") as f:
f.write(
"%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
% (now_dir, now_dir, now_dir, now_dir, now_dir)
)
break
except PermissionError:
pass
from tools import my_utils
import traceback
import shutil
import pdb
import gradio as gr
from subprocess import Popen
import signal
from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
from tools.i18n.i18n import I18nAuto
i18n = I18nAuto()
from scipy.io import wavfile
from tools.my_utils import load_audio
from multiprocessing import cpu_count
# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
n_cpu=cpu_count()
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False
# 判断是否有能用来训练和加速推理的N卡
if torch.cuda.is_available() or ngpu != 0:
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
if any(value in gpu_name.upper()for value in ["10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L4","4060"]):
# A10#A100#V100#A40#P40#M40#K80#A4500
if_gpu_ok = True # 至少有一张能用的N卡
gpu_infos.append("%s\t%s" % (i, gpu_name))
mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
# # 判断是否支持mps加速
# if torch.backends.mps.is_available():
# if_gpu_ok = True
# gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
# mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
if if_gpu_ok and len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
default_batch_size = min(mem) // 2
else:
gpu_info = ("%s\t%s" % ("0", "CPU"))
gpu_infos.append("%s\t%s" % ("0", "CPU"))
default_batch_size = int(psutil.virtual_memory().total/ 1024 / 1024 / 1024 / 2)
gpus = "-".join([i[0] for i in gpu_infos])
pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
def get_weights_names():
SoVITS_names = [pretrained_sovits_name]
for name in os.listdir(SoVITS_weight_root):
if name.endswith(".pth"):SoVITS_names.append(name)
GPT_names = [pretrained_gpt_name]
for name in os.listdir(GPT_weight_root):
if name.endswith(".ckpt"): GPT_names.append(name)
return SoVITS_names,GPT_names
SoVITS_weight_root="SoVITS_weights"
GPT_weight_root="GPT_weights"
os.makedirs(SoVITS_weight_root,exist_ok=True)
os.makedirs(GPT_weight_root,exist_ok=True)
SoVITS_names,GPT_names = get_weights_names()
def custom_sort_key(s):
# 使用正则表达式提取字符串中的数字部分和非数字部分
parts = re.split('(\d+)', s)
# 将数字部分转换为整数，非数字部分保持不变
parts = [int(part) if part.isdigit() else part for part in parts]
return parts
def change_choices():
SoVITS_names, GPT_names = get_weights_names()
return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"}
p_label=None
p_uvr5=None
p_asr=None
p_denoise=None
p_tts_inference=None
def kill_proc_tree(pid, including_parent=True):
try:
parent = psutil.Process(pid)
except psutil.NoSuchProcess:
# Process already terminated
return
children = parent.children(recursive=True)
for child in children:
try:
os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
except OSError:
pass
if including_parent:
try:
os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
except OSError:
pass
system=platform.system()
def kill_process(pid):
if(system=="Windows"):
cmd = "taskkill /t /f /pid %s" % pid
os.system(cmd)
else:
kill_proc_tree(pid)
def change_label(if_label,path_list):
global p_label
if(if_label==True and p_label==None):
path_list=my_utils.clean_path(path_list)
cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s'%(python_exec,path_list,webui_port_subfix,is_share)
yield i18n("打标工具WebUI已开启")
print(cmd)
p_label = Popen(cmd, shell=True)
elif(if_label==False and p_label!=None):
kill_process(p_label.pid)
p_label=None
yield i18n("打标工具WebUI已关闭")
def change_uvr5(if_uvr5):
global p_uvr5
if(if_uvr5==True and p_uvr5==None):
cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share)
yield i18n("UVR5已开启")
print(cmd)
p_uvr5 = Popen(cmd, shell=True)
elif(if_uvr5==False and p_uvr5!=None):
kill_process(p_uvr5.pid)
p_uvr5=None
yield i18n("UVR5已关闭")
def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path):
global p_tts_inference
if(if_tts==True and p_tts_inference==None):
os.environ["gpt_path"]=gpt_path if "/" in gpt_path else "%s/%s"%(GPT_weight_root,gpt_path)
os.environ["sovits_path"]=sovits_path if "/"in sovits_path else "%s/%s"%(SoVITS_weight_root,sovits_path)
os.environ["cnhubert_base_path"]=cnhubert_base_path
os.environ["bert_path"]=bert_path
os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_number
os.environ["is_half"]=str(is_half)
os.environ["infer_ttswebui"]=str(webui_port_infer_tts)
os.environ["is_share"]=str(is_share)
cmd = '"%s" GPT_SoVITS/inference_webui.py'%(python_exec)
yield i18n("TTS推理进程已开启")
print(cmd)
p_tts_inference = Popen(cmd, shell=True)
elif(if_tts==False and p_tts_inference!=None):
kill_process(p_tts_inference.pid)
p_tts_inference=None
yield i18n("TTS推理进程已关闭")
from tools.asr.config import asr_dict
def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
global p_asr
if(p_asr==None):
asr_inp_dir=my_utils.clean_path(asr_inp_dir)
asr_opt_dir=my_utils.clean_path(asr_opt_dir)
cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
cmd += f' -i "{asr_inp_dir}"'
cmd += f' -o "{asr_opt_dir}"'
cmd += f' -s {asr_model_size}'
cmd += f' -l {asr_lang}'
cmd += " -p %s"%("float16"if is_half==True else "float32")
yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
print(cmd)
p_asr = Popen(cmd, shell=True)
p_asr.wait()
p_asr=None
yield f"ASR任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
# return None
def close_asr():
global p_asr
if(p_asr!=None):
kill_process(p_asr.pid)
p_asr=None
return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
def open_denoise(denoise_inp_dir, denoise_opt_dir):
global p_denoise
if(p_denoise==None):
denoise_inp_dir=my_utils.clean_path(denoise_inp_dir)
denoise_opt_dir=my_utils.clean_path(denoise_opt_dir)
cmd = '"%s" tools/cmd-denoise.py -i "%s" -o "%s" -p %s'%(python_exec,denoise_inp_dir,denoise_opt_dir,"float16"if is_half==True else "float32")
yield "语音降噪任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
print(cmd)
p_denoise = Popen(cmd, shell=True)
p_denoise.wait()
p_denoise=None
yield f"语音降噪任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的语音降噪任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
# return None
def close_denoise():
global p_denoise
if(p_denoise!=None):
kill_process(p_denoise.pid)
p_denoise=None
return "已终止语音降噪进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
p_train_SoVITS=None
def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
global p_train_SoVITS
if(p_train_SoVITS==None):
with open("GPT_SoVITS/configs/s2.json")as f:
data=f.read()
data=json.loads(data)
s2_dir="%s/%s"%(exp_root,exp_name)
os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
if(is_half==False):
data["train"]["fp16_run"]=False
batch_size=max(1,batch_size//2)
data["train"]["batch_size"]=batch_size
data["train"]["epochs"]=total_epoch
data["train"]["text_low_lr_rate"]=text_low_lr_rate
data["train"]["pretrained_s2G"]=pretrained_s2G
data["train"]["pretrained_s2D"]=pretrained_s2D
data["train"]["if_save_latest"]=if_save_latest
data["train"]["if_save_every_weights"]=if_save_every_weights
data["train"]["save_every_epoch"]=save_every_epoch
data["train"]["gpu_numbers"]=gpu_numbers1Ba
data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir
data["save_weight_dir"]=SoVITS_weight_root
data["name"]=exp_name
tmp_config_path="%s/tmp_s2.json"%tmp
with open(tmp_config_path,"w")as f:f.write(json.dumps(data))
cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
print(cmd)
p_train_SoVITS = Popen(cmd, shell=True)
p_train_SoVITS.wait()
p_train_SoVITS=None
yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
def close1Ba():
global p_train_SoVITS
if(p_train_SoVITS!=None):
kill_process(p_train_SoVITS.pid)
p_train_SoVITS=None
return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
p_train_GPT=None
def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
global p_train_GPT
if(p_train_GPT==None):
with open("GPT_SoVITS/configs/s1longer.yaml")as f:
data=f.read()
data=yaml.load(data, Loader=yaml.FullLoader)
s1_dir="%s/%s"%(exp_root,exp_name)
os.makedirs("%s/logs_s1"%(s1_dir),exist_ok=True)
if(is_half==False):
data["train"]["precision"]="32"
batch_size = max(1, batch_size // 2)
data["train"]["batch_size"]=batch_size
data["train"]["epochs"]=total_epoch
data["pretrained_s1"]=pretrained_s1
data["train"]["save_every_n_epoch"]=save_every_epoch
data["train"]["if_save_every_weights"]=if_save_every_weights
data["train"]["if_save_latest"]=if_save_latest
data["train"]["if_dpo"]=if_dpo
data["train"]["half_weights_save_dir"]=GPT_weight_root
data["train"]["exp_name"]=exp_name
data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
data["train_phoneme_path"]="%s/2-name2text.txt"%s1_dir
data["output_dir"]="%s/logs_s1"%s1_dir
os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_numbers.replace("-",",")
os.environ["hz"]="25hz"
tmp_config_path="%s/tmp_s1.yaml"%tmp
with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
# cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
print(cmd)
p_train_GPT = Popen(cmd, shell=True)
p_train_GPT.wait()
p_train_GPT=None
yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
def close1Bb():
global p_train_GPT
if(p_train_GPT!=None):
kill_process(p_train_GPT.pid)
p_train_GPT=None
return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
ps_slice=[]
def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
global ps_slice
inp = my_utils.clean_path(inp)
opt_root = my_utils.clean_path(opt_root)
if(os.path.exists(inp)==False):
yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return
if os.path.isfile(inp):n_parts=1
elif os.path.isdir(inp):pass
else:
yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
return
if (ps_slice == []):
for i_part in range(n_parts):
cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s''' % (python_exec,inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts)
print(cmd)
p = Popen(cmd, shell=True)
ps_slice.append(p)
yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps_slice:
p.wait()
ps_slice=[]
yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
def close_slice():
global ps_slice
if (ps_slice != []):
for p_slice in ps_slice:
try:
kill_process(p_slice.pid)
except:
traceback.print_exc()
ps_slice=[]
return "已终止所有切割进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
ps1a=[]
def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir):
global ps1a
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
if (ps1a == []):
opt_dir="%s/%s"%(exp_root,exp_name)
config={
"inp_text":inp_text,
"inp_wav_dir":inp_wav_dir,
"exp_name":exp_name,
"opt_dir":opt_dir,
"bert_pretrained_dir":bert_pretrained_dir,
}
gpu_names=gpu_numbers.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
"is_half": str(is_half)
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1a.append(p)
yield "文本进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1a:
p.wait()
opt = []
for i_part in range(all_parts):
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
with open(txt_path, "r", encoding="utf8") as f:
opt += f.read().strip("\n").split("\n")
os.remove(txt_path)
path_text = "%s/2-name2text.txt" % opt_dir
with open(path_text, "w", encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
ps1a=[]
if len("".join(opt)) > 0:
yield "文本进程成功", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
else:
yield "文本进程失败", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
else:
yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
def close1a():
global ps1a
if (ps1a != []):
for p1a in ps1a:
try:
kill_process(p1a.pid)
except:
traceback.print_exc()
ps1a=[]
return "已终止所有1a进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
ps1b=[]
def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
global ps1b
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
if (ps1b == []):
config={
"inp_text":inp_text,
"inp_wav_dir":inp_wav_dir,
"exp_name":exp_name,
"opt_dir":"%s/%s"%(exp_root,exp_name),
"cnhubert_base_dir":ssl_pretrained_dir,
"is_half": str(is_half)
}
gpu_names=gpu_numbers.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1b.append(p)
yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1b:
p.wait()
ps1b=[]
yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
def close1b():
global ps1b
if (ps1b != []):
for p1b in ps1b:
try:
kill_process(p1b.pid)
except:
traceback.print_exc()
ps1b=[]
return "已终止所有1b进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
ps1c=[]
def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
global ps1c
inp_text = my_utils.clean_path(inp_text)
if (ps1c == []):
opt_dir="%s/%s"%(exp_root,exp_name)
config={
"inp_text":inp_text,
"exp_name":exp_name,
"opt_dir":opt_dir,
"pretrained_s2G":pretrained_s2G_path,
"s2config_path":"GPT_SoVITS/configs/s2.json",
"is_half": str(is_half)
}
gpu_names=gpu_numbers.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1c.append(p)
yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1c:
p.wait()
opt = ["item_name\tsemantic_audio"]
path_semantic = "%s/6-name2semantic.tsv" % opt_dir
for i_part in range(all_parts):
semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
with open(semantic_path, "r", encoding="utf8") as f:
opt += f.read().strip("\n").split("\n")
os.remove(semantic_path)
with open(path_semantic, "w", encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
ps1c=[]
yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
else:
yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
def close1c():
global ps1c
if (ps1c != []):
for p1c in ps1c:
try:
kill_process(p1c.pid)
except:
traceback.print_exc()
ps1c=[]
return "已终止所有语义token进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
ps1abc=[]
def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,ssl_pretrained_dir,pretrained_s2G_path):
global ps1abc
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
if (ps1abc == []):
opt_dir="%s/%s"%(exp_root,exp_name)
try:
#############################1a
path_text="%s/2-name2text.txt" % opt_dir
if(os.path.exists(path_text)==False or (os.path.exists(path_text)==True and len(open(path_text,"r",encoding="utf8").read().strip("\n").split("\n"))<2)):
config={
"inp_text":inp_text,
"inp_wav_dir":inp_wav_dir,
"exp_name":exp_name,
"opt_dir":opt_dir,
"bert_pretrained_dir":bert_pretrained_dir,
"is_half": str(is_half)
}
gpu_names=gpu_numbers1a.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1abc.append(p)
yield "进度：1a-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1abc:p.wait()
opt = []
for i_part in range(all_parts):#txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
with open(txt_path, "r",encoding="utf8") as f:
opt += f.read().strip("\n").split("\n")
os.remove(txt_path)
with open(path_text, "w",encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
assert len("".join(opt)) > 0, "1Aa-文本获取进程失败"
yield "进度：1a-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
ps1abc=[]
#############################1b
config={
"inp_text":inp_text,
"inp_wav_dir":inp_wav_dir,
"exp_name":exp_name,
"opt_dir":opt_dir,
"cnhubert_base_dir":ssl_pretrained_dir,
}
gpu_names=gpu_numbers1Ba.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1abc.append(p)
yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1abc:p.wait()
yield "进度：1a1b-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
ps1abc=[]
#############################1c
path_semantic = "%s/6-name2semantic.tsv" % opt_dir
if(os.path.exists(path_semantic)==False or (os.path.exists(path_semantic)==True and os.path.getsize(path_semantic)<31)):
config={
"inp_text":inp_text,
"exp_name":exp_name,
"opt_dir":opt_dir,
"pretrained_s2G":pretrained_s2G_path,
"s2config_path":"GPT_SoVITS/configs/s2.json",
}
gpu_names=gpu_numbers1c.split("-")
all_parts=len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1abc.append(p)
yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
for p in ps1abc:p.wait()
opt = ["item_name\tsemantic_audio"]
for i_part in range(all_parts):
semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
with open(semantic_path, "r",encoding="utf8") as f:
opt += f.read().strip("\n").split("\n")
os.remove(semantic_path)
with open(path_semantic, "w",encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
yield "进度：all-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
ps1abc = []
yield "一键三连进程结束", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
except:
traceback.print_exc()
close1abc()
yield "一键三连中途报错", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
else:
yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
def close1abc():
global ps1abc
if (ps1abc != []):
for p1abc in ps1abc:
try:
kill_process(p1abc.pid)
except:
traceback.print_exc()
ps1abc=[]
return "已终止所有一键三连进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
gr.Markdown(
value=
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
)
gr.Markdown(
value=
i18n("中文教程文档：https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e")
)
with gr.Tabs():
with gr.TabItem(i18n("0-前置数据集获取工具")):#提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
gr.Markdown(value=i18n("0a-UVR5人声伴奏分离&去混响去延迟工具"))
with gr.Row():
if_uvr5 = gr.Checkbox(label=i18n("是否开启UVR5-WebUI"),show_label=True)
uvr5_info = gr.Textbox(label=i18n("UVR5进程输出信息"))
gr.Markdown(value=i18n("0b-语音切分工具"))
with gr.Row():
with gr.Row():
slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"),value="")
slice_opt_root=gr.Textbox(label=i18n("切分后的子音频的输出根目录"),value="output/slicer_opt")
threshold=gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"),value="-34")
min_length=gr.Textbox(label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),value="4000")
min_interval=gr.Textbox(label=i18n("min_interval:最短切割间隔"),value="300")
hop_size=gr.Textbox(label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),value="10")
max_sil_kept=gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"),value="500")
with gr.Row():
open_slicer_button=gr.Button(i18n("开启语音切割"), variant="primary",visible=True)
close_slicer_button=gr.Button(i18n("终止语音切割"), variant="primary",visible=False)
_max=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("max:归一化后最大值多少"),value=0.9,interactive=True)
alpha=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("alpha_mix:混多少比例归一化后音频进来"),value=0.25,interactive=True)
n_process=gr.Slider(minimum=1,maximum=n_cpu,step=1,label=i18n("切割使用的进程数"),value=4,interactive=True)
slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))
gr.Markdown(value=i18n("0bb-语音降噪工具"))
with gr.Row():
open_denoise_button = gr.Button(i18n("开启语音降噪"), variant="primary",visible=True)
close_denoise_button = gr.Button(i18n("终止语音降噪进程"), variant="primary",visible=False)
denoise_input_dir=gr.Textbox(label=i18n("降噪音频文件输入文件夹"),value="")
denoise_output_dir=gr.Textbox(label=i18n("降噪结果输出文件夹"),value="output/denoise_opt")
denoise_info = gr.Textbox(label=i18n("语音降噪进程输出信息"))
gr.Markdown(value=i18n("0c-中文批量离线ASR工具"))
with gr.Row():
open_asr_button = gr.Button(i18n("开启离线批量ASR"), variant="primary",visible=True)
close_asr_button = gr.Button(i18n("终止ASR进程"), variant="primary",visible=False)
with gr.Column():
with gr.Row():
asr_inp_dir = gr.Textbox(
label=i18n("输入文件夹路径"),
value="D:\\GPT-SoVITS\\raw\\xxx",
interactive=True,
)
asr_opt_dir = gr.Textbox(
label = i18n("输出文件夹路径"),
value = "output/asr_opt",
interactive = True,
)
with gr.Row():
asr_model = gr.Dropdown(
label = i18n("ASR 模型"),
choices = list(asr_dict.keys()),
interactive = True,
value="达摩 ASR (中文)"
)
asr_size = gr.Dropdown(
label = i18n("ASR 模型尺寸"),
choices = ["large"],
interactive = True,
value="large"
)
asr_lang = gr.Dropdown(
label = i18n("ASR 语言设置"),
choices = ["zh"],
interactive = True,
value="zh"
)
with gr.Row():
asr_info = gr.Textbox(label=i18n("ASR进程输出信息"))
def change_lang_choices(key): #根据选择的模型修改可选的语言
# return gr.Dropdown(choices=asr_dict[key]['lang'])
return {"__type__": "update", "choices": asr_dict[key]['lang'],"value":asr_dict[key]['lang'][0]}
def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸
# return gr.Dropdown(choices=asr_dict[key]['size'])
return {"__type__": "update", "choices": asr_dict[key]['size']}
asr_model.change(change_lang_choices, [asr_model], [asr_lang])
asr_model.change(change_size_choices, [asr_model], [asr_size])
gr.Markdown(value=i18n("0d-语音文本校对标注工具"))
with gr.Row():
if_label = gr.Checkbox(label=i18n("是否开启打标WebUI"),show_label=True)
path_list = gr.Textbox(
label=i18n(".list标注文件的路径"),
value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list",
interactive=True,
)
label_info = gr.Textbox(label=i18n("打标工具进程输出信息"))
if_label.change(change_label, [if_label,path_list], [label_info])
if_uvr5.change(change_uvr5, [if_uvr5], [uvr5_info])
open_asr_button.click(open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang], [asr_info,open_asr_button,close_asr_button])
close_asr_button.click(close_asr, [], [asr_info,open_asr_button,close_asr_button])
open_slicer_button.click(open_slice, [slice_inp_path,slice_opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_process], [slicer_info,open_slicer_button,close_slicer_button])
close_slicer_button.click(close_slice, [], [slicer_info,open_slicer_button,close_slicer_button])
open_denoise_button.click(open_denoise, [denoise_input_dir,denoise_output_dir], [denoise_info,open_denoise_button,close_denoise_button])
close_denoise_button.click(close_denoise, [], [denoise_info,open_denoise_button,close_denoise_button])
with gr.TabItem(i18n("1-GPT-SoVITS-TTS")):
with gr.Row():
exp_name = gr.Textbox(label=i18n("*实验/模型名"), value="xxx", interactive=True)
gpu_info = gr.Textbox(label=i18n("显卡信息"), value=gpu_info, visible=True, interactive=False)
pretrained_s2G = gr.Textbox(label=i18n("预训练的SoVITS-G模型路径"), value="GPT_SoVITS/pretrained_models/s2G488k.pth", interactive=True)
pretrained_s2D = gr.Textbox(label=i18n("预训练的SoVITS-D模型路径"), value="GPT_SoVITS/pretrained_models/s2D488k.pth", interactive=True)
pretrained_s1 = gr.Textbox(label=i18n("预训练的GPT模型路径"), value="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", interactive=True)
with gr.TabItem(i18n("1A-训练集格式化工具")):
gr.Markdown(value=i18n("输出logs/实验名目录下应有23456开头的文件和文件夹"))
with gr.Row():
inp_text = gr.Textbox(label=i18n("*文本标注文件"),value=r"D:\RVC1006\GPT-SoVITS\raw\xxx.list",interactive=True)
inp_wav_dir = gr.Textbox(
label=i18n("*训练集音频文件目录"),
# value=r"D:\RVC1006\GPT-SoVITS\raw\xxx",
interactive=True,
placeholder=i18n("填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。如果留空则使用.list文件里的绝对全路径。")
)
gr.Markdown(value=i18n("1Aa-文本内容"))
with gr.Row():
gpu_numbers1a = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
bert_pretrained_dir = gr.Textbox(label=i18n("预训练的中文BERT模型路径"),value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",interactive=False)
button1a_open = gr.Button(i18n("开启文本获取"), variant="primary",visible=True)
button1a_close = gr.Button(i18n("终止文本获取进程"), variant="primary",visible=False)
info1a=gr.Textbox(label=i18n("文本进程输出信息"))
gr.Markdown(value=i18n("1Ab-SSL自监督特征提取"))
with gr.Row():
gpu_numbers1Ba = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
cnhubert_base_dir = gr.Textbox(label=i18n("预训练的SSL模型路径"),value="GPT_SoVITS/pretrained_models/chinese-hubert-base",interactive=False)
button1b_open = gr.Button(i18n("开启SSL提取"), variant="primary",visible=True)
button1b_close = gr.Button(i18n("终止SSL提取进程"), variant="primary",visible=False)
info1b=gr.Textbox(label=i18n("SSL进程输出信息"))
gr.Markdown(value=i18n("1Ac-语义token提取"))
with gr.Row():
gpu_numbers1c = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"),value="%s-%s"%(gpus,gpus),interactive=True)
button1c_open = gr.Button(i18n("开启语义token提取"), variant="primary",visible=True)
button1c_close = gr.Button(i18n("终止语义token提取进程"), variant="primary",visible=False)
info1c=gr.Textbox(label=i18n("语义token提取进程输出信息"))
gr.Markdown(value=i18n("1Aabc-训练集格式化一键三连"))
with gr.Row():
button1abc_open = gr.Button(i18n("开启一键三连"), variant="primary",visible=True)
button1abc_close = gr.Button(i18n("终止一键三连"), variant="primary",visible=False)
info1abc=gr.Textbox(label=i18n("一键三连进程输出信息"))
button1a_open.click(open1a, [inp_text,inp_wav_dir,exp_name,gpu_numbers1a,bert_pretrained_dir], [info1a,button1a_open,button1a_close])
button1a_close.click(close1a, [], [info1a,button1a_open,button1a_close])
button1b_open.click(open1b, [inp_text,inp_wav_dir,exp_name,gpu_numbers1Ba,cnhubert_base_dir], [info1b,button1b_open,button1b_close])
button1b_close.click(close1b, [], [info1b,button1b_open,button1b_close])
button1c_open.click(open1c, [inp_text,exp_name,gpu_numbers1c,pretrained_s2G], [info1c,button1c_open,button1c_close])
button1c_close.click(close1c, [], [info1c,button1c_open,button1c_close])
button1abc_open.click(open1abc, [inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G], [info1abc,button1abc_open,button1abc_close])
button1abc_close.click(close1abc, [], [info1abc,button1abc_open,button1abc_close])
with gr.TabItem(i18n("1B-微调训练")):
gr.Markdown(value=i18n("1Ba-SoVITS训练。用于分享的模型文件输出在SoVITS_weights下。"))
with gr.Row():
batch_size = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
total_epoch = gr.Slider(minimum=1,maximum=25,step=1,label=i18n("总训练轮数total_epoch，不建议太高"),value=8,interactive=True)
text_low_lr_rate = gr.Slider(minimum=0.2,maximum=0.6,step=0.05,label=i18n("文本模块学习率权重"),value=0.4,interactive=True)
save_every_epoch = gr.Slider(minimum=1,maximum=25,step=1,label=i18n("保存频率save_every_epoch"),value=4,interactive=True)
if_save_latest = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
if_save_every_weights = gr.Checkbox(label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True)
gpu_numbers1Ba = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"), value="%s" % (gpus), interactive=True)
with gr.Row():
button1Ba_open = gr.Button(i18n("开启SoVITS训练"), variant="primary",visible=True)
button1Ba_close = gr.Button(i18n("终止SoVITS训练"), variant="primary",visible=False)
info1Ba=gr.Textbox(label=i18n("SoVITS训练进程输出信息"))
gr.Markdown(value=i18n("1Bb-GPT训练。用于分享的模型文件输出在GPT_weights下。"))
with gr.Row():
batch_size1Bb = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
total_epoch1Bb = gr.Slider(minimum=2,maximum=50,step=1,label=i18n("总训练轮数total_epoch"),value=15,interactive=True)
if_dpo = gr.Checkbox(label=i18n("是否开启dpo训练选项(实验性)"), value=False, interactive=True, show_label=True)
if_save_latest1Bb = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
if_save_every_weights1Bb = gr.Checkbox(label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True)
save_every_epoch1Bb = gr.Slider(minimum=1,maximum=50,step=1,label=i18n("保存频率save_every_epoch"),value=5,interactive=True)
gpu_numbers1Bb = gr.Textbox(label=i18n("GPU卡号以-分割，每个卡号一个进程"), value="%s" % (gpus), interactive=True)
with gr.Row():
button1Bb_open = gr.Button(i18n("开启GPT训练"), variant="primary",visible=True)
button1Bb_close = gr.Button(i18n("终止GPT训练"), variant="primary",visible=False)
info1Bb=gr.Textbox(label=i18n("GPT训练进程输出信息"))
button1Ba_open.click(open1Ba, [batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D], [info1Ba,button1Ba_open,button1Ba_close])
button1Ba_close.click(close1Ba, [], [info1Ba,button1Ba_open,button1Ba_close])
button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_dpo,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1], [info1Bb,button1Bb_open,button1Bb_close])
button1Bb_close.click(close1Bb, [], [info1Bb,button1Bb_open,button1Bb_close])
with gr.TabItem(i18n("1C-推理")):
gr.Markdown(value=i18n("选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"))
with gr.Row():
GPT_dropdown = gr.Dropdown(label=i18n("*GPT模型列表"), choices=sorted(GPT_names,key=custom_sort_key),value=pretrained_gpt_name,interactive=True)
SoVITS_dropdown = gr.Dropdown(label=i18n("*SoVITS模型列表"), choices=sorted(SoVITS_names,key=custom_sort_key),value=pretrained_sovits_name,interactive=True)
gpu_number_1C=gr.Textbox(label=i18n("GPU卡号,只能填1个整数"), value=gpus, interactive=True)
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
refresh_button.click(fn=change_choices,inputs=[],outputs=[SoVITS_dropdown,GPT_dropdown])
with gr.Row():
if_tts = gr.Checkbox(label=i18n("是否开启TTS推理WebUI"), show_label=True)
tts_info = gr.Textbox(label=i18n("TTS推理WebUI进程输出信息"))
if_tts.change(change_tts_inference, [if_tts,bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown], [tts_info])
with gr.TabItem(i18n("2-GPT-SoVITS-变声")):gr.Markdown(value=i18n("施工中，请静候佳音"))
app.queue(concurrency_count=511, max_size=1022).launch(
server_name="0.0.0.0",
inbrowser=True,
share=is_share,
server_port=webui_port_main,
quiet=True,
)
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