import os,sys if len(sys.argv)==1:sys.argv.append('v2') version="v1"if sys.argv[1]=="v1" else"v2" os.environ["version"]=version now_dir = os.getcwd() sys.path.insert(0, now_dir) import warnings warnings.filterwarnings("ignore") import json,yaml,torch,pdb,re,shutil import platform import psutil import signal os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO' 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 import traceback 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/runtime\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5" "%s\n%s/GPT_SoVITS/BigVGAN\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5" % (now_dir, now_dir, now_dir, now_dir, now_dir, now_dir) ) break except PermissionError as e: traceback.print_exc() from tools import my_utils import shutil import pdb import subprocess 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, scan_language_list language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto" os.environ["language"]=language i18n = I18nAuto(language=language) from scipy.io import wavfile from tools.my_utils import load_audio, check_for_existance, check_details 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 try: import gradio.analytics as analytics analytics.version_check = lambda:None except:... import gradio as gr 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 ok_gpu_keywords={"10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L4","4060","H","600","506","507","508","509"} set_gpu_numbers=set() 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 ok_gpu_keywords): # 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)) set_gpu_numbers.add(i) 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 def set_default(): global default_batch_size,default_max_batch_size,gpu_info,default_sovits_epoch,default_sovits_save_every_epoch,max_sovits_epoch,max_sovits_save_every_epoch,default_batch_size_s1,if_force_ckpt if_force_ckpt = False if if_gpu_ok and len(gpu_infos) > 0: gpu_info = "\n".join(gpu_infos) minmem = min(mem) # if version == "v3" and minmem < 14: # # API读取不到共享显存,直接填充确认 # try: # torch.zeros((1024,1024,1024,14),dtype=torch.int8,device="cuda") # torch.cuda.empty_cache() # minmem = 14 # except RuntimeError as _: # # 强制梯度检查只需要12G显存 # if minmem >= 12 : # if_force_ckpt = True # minmem = 14 # else: # try: # torch.zeros((1024,1024,1024,12),dtype=torch.int8,device="cuda") # torch.cuda.empty_cache() # if_force_ckpt = True # minmem = 14 # except RuntimeError as _: # print("显存不足以开启V3训练") default_batch_size = minmem // 2 if version!="v3"else minmem//8 default_batch_size_s1=minmem // 2 else: gpu_info = ("%s\t%s" % ("0", "CPU")) gpu_infos.append("%s\t%s" % ("0", "CPU")) set_gpu_numbers.add(0) default_batch_size = default_batch_size_s1 = int(psutil.virtual_memory().total/ 1024 / 1024 / 1024 / 4) if version!="v3": default_sovits_epoch=8 default_sovits_save_every_epoch=4 max_sovits_epoch=25#40 max_sovits_save_every_epoch=25#10 else: default_sovits_epoch=2 default_sovits_save_every_epoch=1 max_sovits_epoch=3#40 max_sovits_save_every_epoch=3#10 default_batch_size = max(1, default_batch_size) default_batch_size_s1 = max(1, default_batch_size_s1) default_max_batch_size = default_batch_size * 3 set_default() gpus = "-".join([i[0] for i in gpu_infos]) default_gpu_numbers=str(sorted(list(set_gpu_numbers))[0]) def fix_gpu_number(input):#将越界的number强制改到界内 try: if(int(input)not in set_gpu_numbers):return default_gpu_numbers except:return input return input def fix_gpu_numbers(inputs): output=[] try: for input in inputs.split(","):output.append(str(fix_gpu_number(input))) return ",".join(output) except: return inputs pretrained_sovits_name=["GPT_SoVITS/pretrained_models/s2G488k.pth", "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth","GPT_SoVITS/pretrained_models/s2Gv3.pth"] pretrained_gpt_name=["GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt","GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1v3.ckpt"] pretrained_model_list = (pretrained_sovits_name[int(version[-1])-1],pretrained_sovits_name[int(version[-1])-1].replace("s2G","s2D"),pretrained_gpt_name[int(version[-1])-1],"GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large","GPT_SoVITS/pretrained_models/chinese-hubert-base") _ = '' for i in pretrained_model_list: if "s2Dv3" not in i and os.path.exists(i) == False: _ += f'\n {i}' if _: print("warning: ", i18n('以下模型不存在:') + _) _ = [[],[]] for i in range(3): if os.path.exists(pretrained_gpt_name[i]):_[0].append(pretrained_gpt_name[i]) else:_[0].append("")##没有下pretrained模型的，说不定他们是想自己从零训底模呢 if os.path.exists(pretrained_sovits_name[i]):_[-1].append(pretrained_sovits_name[i]) else:_[-1].append("") pretrained_gpt_name,pretrained_sovits_name = _ SoVITS_weight_root=["SoVITS_weights","SoVITS_weights_v2","SoVITS_weights_v3"] GPT_weight_root=["GPT_weights","GPT_weights_v2","GPT_weights_v3"] for root in SoVITS_weight_root+GPT_weight_root: os.makedirs(root,exist_ok=True) def get_weights_names(): SoVITS_names = [name for name in pretrained_sovits_name if name!=""] for path in SoVITS_weight_root: for name in os.listdir(path): if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name)) GPT_names = [name for name in pretrained_gpt_name if name!=""] for path in GPT_weight_root: for name in os.listdir(path): if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name)) return SoVITS_names, GPT_names SoVITS_names,GPT_names = get_weights_names() for path in SoVITS_weight_root+GPT_weight_root: os.makedirs(path,exist_ok=True) 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, process_name=""): if(system=="Windows"): cmd = "taskkill /t /f /pid %s" % pid # os.system(cmd) subprocess.run(cmd,shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) else: kill_proc_tree(pid) print(process_name + i18n("进程已终止")) def process_info(process_name="", indicator=""): if indicator == "opened": return process_name + i18n("已开启") elif indicator == "open": return i18n("开启") + process_name elif indicator == "closed": return process_name + i18n("已关闭") elif indicator == "close": return i18n("关闭") + process_name elif indicator == "running": return process_name + i18n("运行中") elif indicator == "occupy": return process_name + i18n("占用中") + "," + i18n("需先终止才能开启下一次任务") elif indicator == "finish": return process_name + i18n("已完成") elif indicator == "failed": return process_name + i18n("失败") elif indicator == "info": return process_name + i18n("进程输出信息") else: return process_name process_name_subfix = i18n("音频标注WebUI") def change_label(path_list): global p_label if p_label is None: check_for_existance([path_list]) 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 process_info(process_name_subfix, "opened"), {'__type__':'update','visible':False}, {'__type__':'update','visible':True} print(cmd) p_label = Popen(cmd, shell=True) else: kill_process(p_label.pid, process_name_subfix) p_label = None yield process_info(process_name_subfix, "closed"), {'__type__':'update','visible':True}, {'__type__':'update','visible':False} process_name_uvr5 = i18n("人声分离WebUI") def change_uvr5(): global p_uvr5 if p_uvr5 is None: cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share) yield process_info(process_name_uvr5, "opened"), {'__type__':'update','visible':False}, {'__type__':'update','visible':True} print(cmd) p_uvr5 = Popen(cmd, shell=True) else: kill_process(p_uvr5.pid, process_name_uvr5) p_uvr5 = None yield process_info(process_name_uvr5, "closed"), {'__type__':'update','visible':True}, {'__type__':'update','visible':False} process_name_tts = i18n("TTS推理WebUI") def change_tts_inference(bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path, batched_infer_enabled): global p_tts_inference if batched_infer_enabled: cmd = '"%s" GPT_SoVITS/inference_webui_fast.py "%s"'%(python_exec, language) else: cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language) # #####v3暂不支持加速推理 # if version=="v3": # cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language) if p_tts_inference is 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"]=fix_gpu_number(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) yield process_info(process_name_tts, "opened"), {'__type__':'update','visible':False}, {'__type__':'update','visible':True} print(cmd) p_tts_inference = Popen(cmd, shell=True) else: kill_process(p_tts_inference.pid, process_name_tts) p_tts_inference = None yield process_info(process_name_tts, "closed"), {'__type__':'update','visible':True}, {'__type__':'update','visible':False} from tools.asr.config import asr_dict process_name_asr = i18n("语音识别") def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision): global p_asr if p_asr is None: asr_inp_dir=my_utils.clean_path(asr_inp_dir) asr_opt_dir=my_utils.clean_path(asr_opt_dir) check_for_existance([asr_inp_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 += f" -p {asr_precision}" output_file_name = os.path.basename(asr_inp_dir) output_folder = asr_opt_dir or "output/asr_opt" output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list') yield process_info(process_name_asr, "opened"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} print(cmd) p_asr = Popen(cmd, shell=True) p_asr.wait() p_asr = None yield process_info(process_name_asr, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}, {"__type__": "update", "value": output_file_path}, {"__type__": "update", "value": output_file_path}, {"__type__": "update", "value": asr_inp_dir} else: yield process_info(process_name_asr, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} def close_asr(): global p_asr if p_asr is not None: kill_process(p_asr.pid, process_name_asr) p_asr = None return process_info(process_name_asr, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} process_name_denoise = i18n("语音降噪") 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) check_for_existance([denoise_inp_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 process_info(process_name_denoise, "opened"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"} print(cmd) p_denoise = Popen(cmd, shell=True) p_denoise.wait() p_denoise=None yield process_info(process_name_denoise, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}, {"__type__": "update", "value": denoise_opt_dir}, {"__type__": "update", "value": denoise_opt_dir} else: yield process_info(process_name_denoise, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"} def close_denoise(): global p_denoise if p_denoise is not None: kill_process(p_denoise.pid, process_name_denoise) p_denoise = None return process_info(process_name_denoise, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} p_train_SoVITS=None process_name_sovits = i18n("SoVITS训练") 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,if_grad_ckpt,lora_rank): 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_%s"%(s2_dir,version),exist_ok=True) if check_for_existance([s2_dir],is_train=True): check_details([s2_dir],is_train=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["train"]["grad_ckpt"]=if_grad_ckpt data["train"]["lora_rank"]=lora_rank data["model"]["version"]=version data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir data["save_weight_dir"]=SoVITS_weight_root[int(version[-1])-1] data["name"]=exp_name data["version"]=version tmp_config_path="%s/tmp_s2.json"%tmp with open(tmp_config_path,"w")as f:f.write(json.dumps(data)) if version in ["v1","v2"]: cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path) else: cmd = '"%s" GPT_SoVITS/s2_train_v3_lora.py --config "%s"'%(python_exec,tmp_config_path) yield process_info(process_name_sovits, "opened"), {"__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 process_info(process_name_sovits, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_sovits, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1Ba(): global p_train_SoVITS if p_train_SoVITS is not None: kill_process(p_train_SoVITS.pid, process_name_sovits) p_train_SoVITS = None return process_info(process_name_sovits, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} p_train_GPT=None process_name_gpt = i18n("GPT训练") 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"if version=="v1"else "GPT_SoVITS/configs/s1longer-v2.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 check_for_existance([s1_dir],is_train=True): check_details([s1_dir],is_train=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[int(version[-1])-1] 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_%s"%(s1_dir,version) # data["version"]=version os.environ["_CUDA_VISIBLE_DEVICES"]=fix_gpu_numbers(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 process_info(process_name_gpt, "opened"), {"__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 process_info(process_name_gpt, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_gpt, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1Bb(): global p_train_GPT if p_train_GPT is not None: kill_process(p_train_GPT.pid, process_name_gpt) p_train_GPT = None return process_info(process_name_gpt, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} ps_slice=[] process_name_slice = i18n("语音切分") 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) check_for_existance([inp]) if(os.path.exists(inp)==False): yield i18n("输入路径不存在"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} return if os.path.isfile(inp):n_parts=1 elif os.path.isdir(inp):pass else: yield i18n("输入路径存在但不可用"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} 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 process_info(process_name_slice, "opened"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} for p in ps_slice: p.wait() ps_slice=[] yield process_info(process_name_slice, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}, {"__type__": "update", "value": opt_root}, {"__type__": "update", "value": opt_root}, {"__type__": "update", "value": opt_root} else: yield process_info(process_name_slice, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} def close_slice(): global ps_slice if (ps_slice != []): for p_slice in ps_slice: try: kill_process(p_slice.pid, process_name_slice) except: traceback.print_exc() ps_slice=[] return process_info(process_name_slice, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} ps1a=[] process_name_1a = i18n("文本分词与特征提取") 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 check_for_existance([inp_text,inp_wav_dir], is_dataset_processing=True): check_details([inp_text,inp_wav_dir], is_dataset_processing=True) 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": fix_gpu_number(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 process_info(process_name_1a, "running"), {"__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 process_info(process_name_1a, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_1a, "failed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_1a, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1a(): global ps1a if ps1a != []: for p1a in ps1a: try: kill_process(p1a.pid, process_name_1a) except: traceback.print_exc() ps1a = [] return process_info(process_name_1a, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} ps1b=[] process_name_1b = i18n("语音自监督特征提取") 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 check_for_existance([inp_text,inp_wav_dir], is_dataset_processing=True): check_details([inp_text,inp_wav_dir], is_dataset_processing=True) 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": fix_gpu_number(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 process_info(process_name_1b, "running"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} for p in ps1b: p.wait() ps1b=[] yield process_info(process_name_1b, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_1b, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1b(): global ps1b if (ps1b != []): for p1b in ps1b: try: kill_process(p1b.pid, process_name_1b) except: traceback.print_exc() ps1b=[] return process_info(process_name_1b, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} ps1c=[] process_name_1c = i18n("语义Token提取") def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path): global ps1c inp_text = my_utils.clean_path(inp_text) if check_for_existance([inp_text,''], is_dataset_processing=True): check_details([inp_text,''], is_dataset_processing=True) 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": fix_gpu_number(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 process_info(process_name_1c, "running"), {"__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 process_info(process_name_1c, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_1c, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1c(): global ps1c if (ps1c != []): for p1c in ps1c: try: kill_process(p1c.pid, process_name_1c) except: traceback.print_exc() ps1c=[] return process_info(process_name_1c, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} ps1abc=[] process_name_1abc = i18n("训练集格式化一键三连") 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 check_for_existance([inp_text,inp_wav_dir], is_dataset_processing=True): check_details([inp_text,inp_wav_dir], is_dataset_processing=True) 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": fix_gpu_number(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 i18n("进度") + ": 1A-Doing", {"__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, process_info(process_name_1a, "failed") yield i18n("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": fix_gpu_number(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 i18n("进度") + ": 1A-Done, 1B-Doing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} for p in ps1abc:p.wait() yield i18n("进度") + ": 1A-Done, 1B-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": fix_gpu_number(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 i18n("进度") + ": 1A-Done, 1B-Done, 1C-Doing", {"__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 i18n("进度") + ": 1A-Done, 1B-Done, 1C-Done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} ps1abc = [] yield process_info(process_name_1abc, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} except: traceback.print_exc() close1abc() yield process_info(process_name_1abc, "failed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: yield process_info(process_name_1abc, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} def close1abc(): global ps1abc if (ps1abc != []): for p1abc in ps1abc: try: kill_process(p1abc.pid, process_name_1abc) except: traceback.print_exc() ps1abc=[] return process_info(process_name_1abc, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} def switch_version(version_): os.environ["version"]=version_ global version version = version_ if pretrained_sovits_name[int(version[-1])-1] !='' and pretrained_gpt_name[int(version[-1])-1] !='':... else: gr.Warning(i18n('未下载模型') + ": " + version.upper()) set_default() return {'__type__': 'update', 'value': pretrained_sovits_name[int(version[-1])-1]}, \ {'__type__': 'update', 'value': pretrained_sovits_name[int(version[-1])-1].replace("s2G","s2D")}, \ {'__type__': 'update', 'value': pretrained_gpt_name[int(version[-1])-1]}, \ {'__type__': 'update', 'value': pretrained_gpt_name[int(version[-1])-1]}, \ {'__type__': 'update', 'value': pretrained_sovits_name[int(version[-1])-1]}, \ {'__type__': 'update', "value": default_batch_size, "maximum": default_max_batch_size}, \ {'__type__': 'update', "value": default_sovits_epoch, "maximum": max_sovits_epoch}, \ {'__type__': 'update', "value": default_sovits_save_every_epoch,"maximum": max_sovits_save_every_epoch}, \ {'__type__': 'update', "visible": True if version!="v3"else False}, \ {'__type__': 'update', "value": False if not if_force_ckpt else True, "interactive": True if not if_force_ckpt else False}, \ {'__type__': 'update', "interactive": True, "value": False}, \ {'__type__': 'update', "visible": True if version== "v3" else False} # {'__type__': 'update', "interactive": False if version == "v3" else True, "value": False}, \ ####batch infer if os.path.exists('GPT_SoVITS/text/G2PWModel'):... else: cmd = '"%s" GPT_SoVITS/download.py'%python_exec p = Popen(cmd, shell=True) p.wait() def sync(text): return {'__type__': 'update', 'value': text} 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.") + "
" + i18n("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("0-"+i18n("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="0a-"+i18n("UVR5人声伴奏分离&去混响去延迟工具")) with gr.Row(): with gr.Column(scale=3): with gr.Row(): uvr5_info = gr.Textbox(label=process_info(process_name_uvr5, "info")) open_uvr5 = gr.Button(value=process_info(process_name_uvr5, "open"),variant="primary",visible=True) close_uvr5 = gr.Button(value=process_info(process_name_uvr5, "close"),variant="primary",visible=False) gr.Markdown(value="0b-"+i18n("语音切分工具")) with gr.Row(): with gr.Column(scale=3): 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") with gr.Row(): 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(): _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) with gr.Row(): 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=process_info(process_name_slice, "info")) open_slicer_button = gr.Button(value=process_info(process_name_slice, "open"),variant="primary",visible=True) close_slicer_button = gr.Button(value=process_info(process_name_slice, "close"),variant="primary",visible=False) gr.Markdown(value="0bb-"+i18n("语音降噪工具")) with gr.Row(): with gr.Column(scale=3): with gr.Row(): denoise_input_dir=gr.Textbox(label=i18n("输入文件夹路径"),value="") denoise_output_dir=gr.Textbox(label=i18n("输出文件夹路径"),value="output/denoise_opt") with gr.Row(): denoise_info = gr.Textbox(label=process_info(process_name_denoise, "info")) open_denoise_button = gr.Button(value=process_info(process_name_denoise, "open"),variant="primary",visible=True) close_denoise_button = gr.Button(value=process_info(process_name_denoise, "close"),variant="primary",visible=False) gr.Markdown(value="0c-"+i18n("语音识别工具")) with gr.Row(): with gr.Column(scale=3): 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","yue"], interactive=True, value="zh") asr_precision = gr.Dropdown(label=i18n("数据类型精度"), choices=["float32"], interactive=True, value="float32") with gr.Row(): asr_info = gr.Textbox(label=process_info(process_name_asr, "info")) open_asr_button = gr.Button(value=process_info(process_name_asr, "open"),variant="primary",visible=True) close_asr_button = gr.Button(value=process_info(process_name_asr, "close"),variant="primary",visible=False) def change_lang_choices(key): #根据选择的模型修改可选的语言 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 {"__type__": "update", "choices": asr_dict[key]['size'], "value": asr_dict[key]['size'][-1]} def change_precision_choices(key): #根据选择的模型修改可选的语言 if key =="Faster Whisper (多语种)": if default_batch_size <= 4: precision = 'int8' elif is_half: precision = 'float16' else: precision = 'float32' else: precision = 'float32' return {"__type__": "update", "choices": asr_dict[key]['precision'], "value": precision} asr_model.change(change_lang_choices, [asr_model], [asr_lang]) asr_model.change(change_size_choices, [asr_model], [asr_size]) asr_model.change(change_precision_choices, [asr_model], [asr_precision]) gr.Markdown(value="0d-"+i18n("语音文本校对标注工具")) with gr.Row(): with gr.Column(scale=3): with gr.Row(): path_list = gr.Textbox(label=i18n("标注文件路径 (含文件后缀 *.list)"), value="D:\\RVC1006\\GPT-SoVITS\\raw\\xxx.list", interactive=True) label_info = gr.Textbox(label=process_info(process_name_subfix, "info")) open_label = gr.Button(value=process_info(process_name_subfix, "open"),variant="primary",visible=True) close_label = gr.Button(value=process_info(process_name_subfix, "close"),variant="primary",visible=False) open_label.click(change_label, [path_list], [label_info,open_label,close_label]) close_label.click(change_label, [path_list], [label_info,open_label,close_label]) open_uvr5.click(change_uvr5, [], [uvr5_info,open_uvr5,close_uvr5]) close_uvr5.click(change_uvr5, [], [uvr5_info,open_uvr5,close_uvr5]) with gr.TabItem(i18n("1-GPT-SoVITS-TTS")): with gr.Row(): 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) version_checkbox = gr.Radio(label=i18n("版本"),value=version,choices=['v1','v2','v3']) with gr.Row(): pretrained_s2G = gr.Textbox(label=i18n("预训练SoVITS-G模型路径"), value=pretrained_sovits_name[int(version[-1])-1], interactive=True, lines=2, max_lines=3,scale=9) pretrained_s2D = gr.Textbox(label=i18n("预训练SoVITS-D模型路径"), value=pretrained_sovits_name[int(version[-1])-1].replace("s2G","s2D"), interactive=True, lines=2, max_lines=3,scale=9) pretrained_s1 = gr.Textbox(label=i18n("预训练GPT模型路径"), value=pretrained_gpt_name[int(version[-1])-1], interactive=True, lines=2, max_lines=3,scale=10) with gr.TabItem("1A-"+i18n("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(): 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,scale=10) with gr.Row(): 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。"), scale=10 ) gr.Markdown(value="1Aa-"+process_name_1a) with gr.Row(): 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) with gr.Row(): bert_pretrained_dir = gr.Textbox(label=i18n("预训练中文BERT模型路径"),value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",interactive=False,lines=2) with gr.Row(): button1a_open = gr.Button(value=process_info(process_name_1a, "open"),variant="primary",visible=True) button1a_close = gr.Button(value=process_info(process_name_1a, "close"),variant="primary",visible=False) with gr.Row(): info1a=gr.Textbox(label=process_info(process_name_1a, "info")) gr.Markdown(value="1Ab-"+process_name_1b) with gr.Row(): 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) with gr.Row(): cnhubert_base_dir = gr.Textbox(label=i18n("预训练SSL模型路径"),value="GPT_SoVITS/pretrained_models/chinese-hubert-base",interactive=False,lines=2) with gr.Row(): button1b_open = gr.Button(value=process_info(process_name_1b, "open"),variant="primary",visible=True) button1b_close = gr.Button(value=process_info(process_name_1b, "close"),variant="primary",visible=False) with gr.Row(): info1b=gr.Textbox(label=process_info(process_name_1b, "info")) gr.Markdown(value="1Ac-"+process_name_1c) with gr.Row(): 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) with gr.Row(): pretrained_s2G_ = gr.Textbox(label=i18n("预训练SoVITS-G模型路径"), value=pretrained_sovits_name[int(version[-1])-1], interactive=False,lines=2) with gr.Row(): button1c_open = gr.Button(value=process_info(process_name_1c, "open"),variant="primary",visible=True) button1c_close = gr.Button(value=process_info(process_name_1c, "close"),variant="primary",visible=False) with gr.Row(): info1c=gr.Textbox(label=process_info(process_name_1c, "info")) gr.Markdown(value="1Aabc-"+process_name_1abc) with gr.Row(): with gr.Row(): button1abc_open = gr.Button(value=process_info(process_name_1abc, "open"),variant="primary",visible=True) button1abc_close = gr.Button(value=process_info(process_name_1abc, "close"),variant="primary",visible=False) with gr.Row(): info1abc=gr.Textbox(label=process_info(process_name_1abc, "info")) pretrained_s2G.change(sync,[pretrained_s2G],[pretrained_s2G_]) open_asr_button.click(open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang, asr_precision], [asr_info,open_asr_button,close_asr_button,path_list,inp_text,inp_wav_dir]) 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,asr_inp_dir,denoise_input_dir,inp_wav_dir]) 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,asr_inp_dir,inp_wav_dir]) close_denoise_button.click(close_denoise, [], [denoise_info,open_denoise_button,close_denoise_button]) 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("1B-"+i18n("微调训练")): gr.Markdown(value="1Ba-"+i18n("SoVITS 训练: 模型权重文件在 SoVITS_weights/")) with gr.Row(): with gr.Column(): with gr.Row(): batch_size = gr.Slider(minimum=1,maximum=default_max_batch_size,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True) total_epoch = gr.Slider(minimum=1,maximum=max_sovits_epoch,step=1,label=i18n("总训练轮数total_epoch，不建议太高"),value=default_sovits_epoch,interactive=True) with gr.Row(): 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,visible=True if version!="v3"else False)#v3 not need lora_rank = gr.Radio(label=i18n("LoRA秩"), value="32", choices=['16', '32', '64', '128'],visible=True if version=="v3"else False)#v1v2 not need save_every_epoch = gr.Slider(minimum=1,maximum=max_sovits_save_every_epoch,step=1,label=i18n("保存频率save_every_epoch"),value=default_sovits_save_every_epoch,interactive=True) with gr.Column(): with gr.Column(): if_save_latest = gr.Checkbox(label=i18n("是否仅保存最新的权重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) if_grad_ckpt = gr.Checkbox(label="v3是否开启梯度检查点节省显存占用", value=False, interactive=True if version == "v3" else False, show_label=True,visible=False) # 只有V3s2可以用 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" % (gpus), interactive=True) with gr.Row(): with gr.Row(): button1Ba_open = gr.Button(value=process_info(process_name_sovits, "open"),variant="primary",visible=True) button1Ba_close = gr.Button(value=process_info(process_name_sovits, "close"),variant="primary",visible=False) with gr.Row(): info1Ba=gr.Textbox(label=process_info(process_name_sovits, "info")) gr.Markdown(value="1Bb-"+i18n("GPT 训练: 模型权重文件在 GPT_weights/")) with gr.Row(): with gr.Column(): 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_s1,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) with gr.Row(): save_every_epoch1Bb = gr.Slider(minimum=1,maximum=50,step=1,label=i18n("保存频率save_every_epoch"),value=5,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) with gr.Column(): with gr.Column(): if_save_latest1Bb = gr.Checkbox(label=i18n("是否仅保存最新的权重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) with gr.Row(): 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(): with gr.Row(): button1Bb_open = gr.Button(value=process_info(process_name_gpt, "open"),variant="primary",visible=True) button1Bb_close = gr.Button(value=process_info(process_name_gpt, "close"),variant="primary",visible=False) with gr.Row(): info1Bb=gr.Textbox(label=process_info(process_name_gpt, "info")) 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,if_grad_ckpt,lora_rank], [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("1C-"+i18n("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(): 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[0],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[0],interactive=True) with gr.Row(): 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(): with gr.Row(): batched_infer_enabled = gr.Checkbox(label=i18n("启用并行推理版本"), value=False, interactive=True, show_label=True) with gr.Row(): open_tts = gr.Button(value=process_info(process_name_tts, "open"),variant='primary',visible=True) close_tts = gr.Button(value=process_info(process_name_tts, "close"),variant='primary',visible=False) with gr.Row(): tts_info = gr.Textbox(label=process_info(process_name_tts, "info")) open_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown, batched_infer_enabled], [tts_info,open_tts,close_tts]) close_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown, batched_infer_enabled], [tts_info,open_tts,close_tts]) version_checkbox.change(switch_version,[version_checkbox],[pretrained_s2G,pretrained_s2D,pretrained_s1,GPT_dropdown,SoVITS_dropdown,batch_size,total_epoch,save_every_epoch,text_low_lr_rate, if_grad_ckpt, batched_infer_enabled, lora_rank]) with gr.TabItem(i18n("2-GPT-SoVITS-变声")):gr.Markdown(value=i18n("施工中，请静候佳音")) app.queue().launch(#concurrency_count=511, max_size=1022 server_name="0.0.0.0", inbrowser=True, share=is_share, server_port=webui_port_main, quiet=True, )