diff --git a/.gitignore b/.gitignore index 7662674..bca5a87 100644 --- a/.gitignore +++ b/.gitignore @@ -18,4 +18,3 @@ TEMP weight.json ffmpeg* ffprobe* -zoengjyutgaai* \ No newline at end of file diff --git a/GPT_SoVITS/prepare_datasets/1-get-text.py b/GPT_SoVITS/prepare_datasets/1-get-text.py index 6ca5ded..0d0019a 100644 --- a/GPT_SoVITS/prepare_datasets/1-get-text.py +++ b/GPT_SoVITS/prepare_datasets/1-get-text.py @@ -8,8 +8,8 @@ from time import time as ttime import torch from text.cleaner import clean_text -from transformers import AutoModelForMaskedLM, AutoTokenizer from tqdm import tqdm +from transformers import AutoModelForMaskedLM, AutoTokenizer from tools.my_utils import clean_path diff --git a/webui.py b/webui.py index b73ed89..1bc02e9 100644 --- a/webui.py +++ b/webui.py @@ -1,38 +1,68 @@ -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 +import json +import os +import platform +import re +import shutil +import signal +import site +import subprocess +import sys +import traceback +import warnings +from multiprocessing import cpu_count +from subprocess import Popen + +import gradio as gr +import psutil +import torch +import yaml + +from config import ( + exp_root, + infer_device, + is_half, + is_share, + python_exec, + webui_port_infer_tts, + webui_port_main, + webui_port_subfix, + webui_port_uvr5, +) +from tools import my_utils +from tools.asr.config import asr_dict +from tools.i18n.i18n import I18nAuto, scan_language_list +from tools.my_utils import check_details, check_for_existance + +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)): +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 + 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" +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: @@ -45,29 +75,18 @@ for site_packages_root in site_packages_roots: % (now_dir, now_dir, now_dir, now_dir, now_dir, now_dir) ) break - except PermissionError as e: + except PermissionError: 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 +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() + analytics.version_check = lambda: None +except: + ... +n_cpu = cpu_count() ngpu = torch.cuda.device_count() gpu_infos = [] @@ -75,8 +94,8 @@ 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() +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) @@ -85,15 +104,16 @@ if torch.cuda.is_available() or ngpu != 0: 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)) + 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 + 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) @@ -117,49 +137,58 @@ def set_default(): # 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 + 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 + 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_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 +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 + if (int(input)not in set_gpu_numbers): + return default_gpu_numbers + except: + return input return input + + def fix_gpu_numbers(inputs): - output=[] + output = [] try: - for input in inputs.split(","):output.append(str(fix_gpu_number(input))) + 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") +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: @@ -168,32 +197,42 @@ for i in pretrained_model_list: 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 = _ + 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) + -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!=""] + 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!=""] + 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)) + 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) + +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 @@ -202,15 +241,18 @@ def custom_sort_key(s): 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"} + 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 -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: @@ -231,16 +273,20 @@ def kill_proc_tree(pid, including_parent=True): except OSError: pass -system=platform.system() + +system = platform.system() + + def kill_process(pid, process_name=""): - if(system=="Windows"): + if (system == "Windows"): cmd = "taskkill /t /f /pid %s" % pid # os.system(cmd) - subprocess.run(cmd,shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + 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") @@ -263,69 +309,79 @@ def process_info(process_name="", indicator=""): 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} + 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} + 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} + 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} + 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): + + +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) + 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) + 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} + 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} + 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) + 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}"' @@ -345,6 +401,7 @@ def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_ 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: @@ -352,24 +409,28 @@ def close_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) + 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") + 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 + 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: @@ -377,43 +438,47 @@ def close_denoise(): p_denoise = None return process_info(process_name_denoise, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -p_train_SoVITS=None + +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): + + +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): + 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) + 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) + 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) @@ -423,6 +488,7 @@ def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_s 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: @@ -430,41 +496,45 @@ def close1Ba(): p_train_SoVITS = None return process_info(process_name_sovits, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -p_train_GPT=None + +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): + + +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" + 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["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)) + 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) + 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) @@ -474,6 +544,7 @@ def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_ 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: @@ -481,35 +552,41 @@ def close1Bb(): p_train_GPT = None return process_info(process_name_gpt, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -ps_slice=[] + +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): + + +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): + 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 + 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) + 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=[] + 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 != []): @@ -518,28 +595,31 @@ def close_slice(): kill_process(p_slice.pid, process_name_slice) except: traceback.print_exc() - ps_slice=[] + ps_slice = [] return process_info(process_name_slice, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -ps1a=[] + +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): + + +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 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, + 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) + gpu_names = gpu_numbers.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -550,7 +630,7 @@ def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir): } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec + cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec print(cmd) p = Popen(cmd, shell=True) ps1a.append(p) @@ -566,7 +646,7 @@ def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir): path_text = "%s/2-name2text.txt" % opt_dir with open(path_text, "w", encoding="utf8") as f: f.write("\n".join(opt) + "\n") - ps1a=[] + ps1a = [] if len("".join(opt)) > 0: yield process_info(process_name_1a, "finish"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} else: @@ -574,6 +654,7 @@ def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir): else: yield process_info(process_name_1a, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} + def close1a(): global ps1a if ps1a != []: @@ -585,25 +666,28 @@ def close1a(): ps1a = [] return process_info(process_name_1a, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -ps1b=[] + +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): + + +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 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, + 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) + gpu_names = gpu_numbers.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -613,18 +697,19 @@ def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir): } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec + 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=[] + 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 != []): @@ -633,28 +718,31 @@ def close1b(): kill_process(p1b.pid, process_name_1b) except: traceback.print_exc() - ps1b=[] + ps1b = [] return process_info(process_name_1b, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -ps1c=[] + +ps1c = [] process_name_1c = i18n("语义Token提取") -def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path): + + +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 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", + 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) + gpu_names = gpu_numbers.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -664,7 +752,7 @@ def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path): } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec + cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec print(cmd) p = Popen(cmd, shell=True) ps1c.append(p) @@ -680,11 +768,12 @@ def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path): os.remove(semantic_path) with open(path_semantic, "w", encoding="utf8") as f: f.write("\n".join(opt) + "\n") - ps1c=[] + 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 != []): @@ -693,33 +782,36 @@ def close1c(): kill_process(p1c.pid, process_name_1c) except: traceback.print_exc() - ps1c=[] + ps1c = [] return process_info(process_name_1c, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} -ps1abc=[] + +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): + + +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 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) + 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, + # 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) + gpu_names = gpu_numbers1a.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -729,34 +821,35 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec + 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() + 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) + 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: + 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: + 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, + 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) + gpu_names = gpu_numbers1Ba.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -766,26 +859,27 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec + 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() + for p in ps1abc: + p.wait() yield i18n("进度") + ": 1A-Done, 1B-Done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} - ps1abc=[] - #############################1c + 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", + 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) + gpu_names = gpu_numbers1c.split("-") + all_parts = len(gpu_names) for i_part in range(all_parts): config.update( { @@ -795,20 +889,21 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb } ) os.environ.update(config) - cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec + 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() + 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: + 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: + 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 = [] @@ -820,6 +915,7 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb else: yield process_info(process_name_1abc, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True} + def close1abc(): global ps1abc if (ps1abc != []): @@ -828,92 +924,96 @@ def close1abc(): kill_process(p1abc.pid, process_name_1abc) except: traceback.print_exc() - ps1abc=[] + ps1abc = [] return process_info(process_name_1abc, "closed"), {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False} + def switch_version(version_): - os.environ["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] !='':... + 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]}, \ + 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": 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": False if version == "v3" else True, "value": False}, \ - {'__type__': 'update', "visible": True if version== "v3" else False} + {'__type__': 'update', "visible": True if version == "v3" else False} -if os.path.exists('GPT_SoVITS/text/G2PWModel'):... + +if os.path.exists('GPT_SoVITS/text/G2PWModel'): + ... else: - cmd = '"%s" GPT_SoVITS/download.py'%python_exec + 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.") + 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" + 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.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) + 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")) + 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") + 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") + 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) + _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) + 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) + 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")) + 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") + 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) + 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")) + gr.Markdown(value="0c-" + i18n("语音识别工具")) with gr.Row(): with gr.Column(scale=3): with gr.Row(): @@ -922,19 +1022,21 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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_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) + 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 + 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 + + 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)": + + 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: @@ -948,36 +1050,36 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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")) + 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 = 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]) + 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']) + 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) + 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")): + 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) + 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"), @@ -986,144 +1088,145 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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) + 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) + 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) + 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) + 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")) + info1a = gr.Textbox(label=process_info(process_name_1a, "info")) - gr.Markdown(value="1Ab-"+process_name_1b) + 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) + 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) + 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) + 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")) + info1b = gr.Textbox(label=process_info(process_name_1b, "info")) - gr.Markdown(value="1Ac-"+process_name_1c) + 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) + 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) + 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) + 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")) + info1c = gr.Textbox(label=process_info(process_name_1c, "info")) - gr.Markdown(value="1Aabc-"+process_name_1abc) + 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) + 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")) + 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]) + 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]) + 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.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) + 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) + 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 + 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) + 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/")) + 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) + 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) + 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) + 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) + 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")) + 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]) + 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")): + 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) + 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) + 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]) + 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) + 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]) + 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]) + 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")) + 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 + app.queue().launch( # concurrency_count=511, max_size=1022 server_name="0.0.0.0", inbrowser=True, share=is_share,