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.
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LICENSE.") ) 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, )