Merge d32d70c2b103e1d3e5b17a98d8311daa1db0ed88 into 165882d64f474b3563fa91adc1a679436ae9c3b8

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -1,6 +1,17 @@
 # -*- coding: utf-8 -*-
 
 import os
+import os.path
+import shutil
+import traceback
+from time import time as ttime
+
+import torch
+from text.cleaner import clean_text
+from tqdm import tqdm
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+from tools.my_utils import clean_path
 
 inp_text = os.environ.get("inp_text")
 inp_wav_dir = os.environ.get("inp_wav_dir")
@@ -11,17 +22,8 @@ if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
 opt_dir = os.environ.get("opt_dir")
 bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
-import torch
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 version = os.environ.get('version', None)
-import sys, numpy as np, traceback, pdb
-import os.path
-from glob import glob
-from tqdm import tqdm
-from text.cleaner import clean_text
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-from tools.my_utils import clean_path
 
 # inp_text=sys.argv[1]
 # inp_wav_dir=sys.argv[2]
@@ -32,11 +34,8 @@ from tools.my_utils import clean_path
 # opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
 # bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
 
-from time import time as ttime
-import shutil
 
-
+def my_save(fea, path):  # fix issue: torch.save doesn't support chinese path
-def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
     # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
@@ -56,11 +55,13 @@ if os.path.exists(txt_path) == False:
     #     device = "mps"
     else:
         device = "cpu"
-    if os.path.exists(bert_pretrained_dir):...
+    if os.path.exists(bert_pretrained_dir):
-    else:raise FileNotFoundError(bert_pretrained_dir)
+        ...
+    else:
+        raise FileNotFoundError(bert_pretrained_dir)
     tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
     bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
-    if is_half == True:
+    if is_half:
         bert_model = bert_model.half().to(device)
     else:
         bert_model = bert_model.to(device)
@@ -126,7 +127,7 @@ if os.path.exists(txt_path) == False:
         "YUE": "yue",
         "Yue": "yue",
     }
-    for line in lines[int(i_part) :: int(all_parts)]:
+    for line in tqdm(lines[int(i_part):: int(all_parts)]):
         try:
             wav_name, spk_name, language, text = line.split("|")
             # todo.append([name,text,"zh"])

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -1,6 +1,23 @@
-# -*- coding: utf-8 -*-
+"""
+Step 2 of data preparation: Extract HuBERT features from the audio files,
+and resample the audio to 32kHz and saving it.
+"""
+
+import os
+import shutil
+import sys
+import traceback
+from time import time as ttime
+
+import librosa
+import numpy as np
+import torch
+from feature_extractor import cnhubert
+from scipy.io import wavfile
+from tqdm import tqdm
+
+from tools.my_utils import clean_path, load_audio
 
-import sys,os
 inp_text = os.environ.get("inp_text")
 inp_wav_dir = os.environ.get("inp_wav_dir")
 exp_name = os.environ.get("exp_name")
@@ -8,18 +25,16 @@ i_part=                             os.environ.get("i_part")
 all_parts = os.environ.get("all_parts")
 if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-from feature_extractor import cnhubert
+
 opt_dir = os.environ.get("opt_dir")
 cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
-import torch
+
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 
-import pdb,traceback,numpy as np,logging
+
-from scipy.io import wavfile
-import librosa
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from tools.my_utils import load_audio,clean_path
+
 
 # from config import cnhubert_base_path
 # cnhubert.cnhubert_base_path=cnhubert_base_path
@@ -32,15 +47,14 @@ from tools.my_utils import load_audio,clean_path
 # cnhubert.cnhubert_base_path=sys.argv[7]
 # opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
 
-from time import time as ttime
+def my_save(fea, path):  # fix issue: torch.save doesn't support chinese path
-import shutil
-def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
     # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
-    tmp_path="%s%s.pth"%(ttime(),i_part)
+    tmp_path = f"{ttime()}{i_part}.pth"
     torch.save(fea, tmp_path)
-    shutil.move(tmp_path,"%s/%s"%(dir,name))
+    shutil.move(tmp_path, f"{dir}/{name}")
+
 
 hubert_dir = "%s/4-cnhubert" % (opt_dir)
 wav32dir = "%s/5-wav32k" % (opt_dir)
@@ -56,6 +70,8 @@ if torch.cuda.is_available():
 #     device = "mps"
 else:
     device = "cpu"
+
+
 model = cnhubert.get_model()
 # is_half=False
 if (is_half == True):
@@ -64,47 +80,66 @@ else:
     model = model.to(device)
 
 nan_fails = []
+
+
 def name2go(wav_name, wav_path):
+    """
+    Extract HuBERT features from the audio files, and resample the audio to 32kHz and saving it.
+    """
+    # Skip if the file already exists
     hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
-    if(os.path.exists(hubert_path)):return
+    if (os.path.exists(hubert_path)):
-    tmp_audio = load_audio(wav_path, 32000)
-    tmp_max = np.abs(tmp_audio).max()
-    if tmp_max > 2.2:
-        print("%s-filtered,%s" % (wav_name, tmp_max))
         return
+
+    # Load the audio file in 32kHz sampling rate
+    tmp_audio = load_audio(wav_path, 32000)
+
+    # Check the maximum amplitude of the audio file
+    tmp_max = np.abs(tmp_audio).max()
+    # Skip if the maximum amplitude is too high (volume is too loud)
+    if tmp_max > 2.2:
+        print(f"{wav_name}-filtered,{tmp_max}")
+        return
+    # Normalize the audio
     tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
     tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
     tmp_audio = librosa.resample(
         tmp_audio32b, orig_sr=32000, target_sr=16000
     )  # 不是重采样问题
     tensor_wav16 = torch.from_numpy(tmp_audio)
-    if (is_half == True):
+
+    # if half-precision is enabled, convert the tensor to half-precision
+    if is_half:
         tensor_wav16 = tensor_wav16.half().to(device)
     else:
         tensor_wav16 = tensor_wav16.to(device)
+
+    # Extract HuBERT features from the audio file
     ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu()  # torch.Size([1, 768, 215])
+
     if np.isnan(ssl.detach().numpy()).sum() != 0:
         nan_fails.append((wav_name, wav_path))
-        print("nan filtered:%s"%wav_name)
+        print(f"nan filtered:{wav_name}")
         return
     wavfile.write(
-        "%s/%s"%(wav32dir,wav_name),
+        f"{wav32dir}/{wav_name}",
         32000,
         tmp_audio32.astype("int16"),
     )
     my_save(ssl, hubert_path)
 
+
 with open(inp_text, "r", encoding="utf8")as f:
     lines = f.read().strip("\n").split("\n")
 
-for line in lines[int(i_part)::int(all_parts)]:
+for line in tqdm(lines[int(i_part)::int(all_parts)]):
     try:
         # wav_name,text=line.split("\t")
         wav_name, spk_name, language, text = line.split("|")
         wav_name = clean_path(wav_name)
-        if (inp_wav_dir != "" and inp_wav_dir != None):
+        if (inp_wav_dir != "" and inp_wav_dir is not None):
             wav_name = os.path.basename(wav_name)
-            wav_path = "%s/%s"%(inp_wav_dir, wav_name)
+            wav_path = f"{inp_wav_dir}/{wav_name}"
 
         else:
             wav_path = wav_name
@@ -113,7 +148,7 @@ for line in lines[int(i_part)::int(all_parts)]:
     except:
         print(line, traceback.format_exc())
 
-if(len(nan_fails)>0 and is_half==True):
+if (len(nan_fails) > 0 and is_half):
     is_half = False
     model = model.float()
     for wav in nan_fails:

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -1,4 +1,12 @@
+import logging
 import os
+import sys
+import traceback
+
+import torch
+import utils
+
+from tools.my_utils import clean_path
 
 inp_text = os.environ.get("inp_text")
 exp_name = os.environ.get("exp_name")
@@ -10,8 +18,10 @@ opt_dir = os.environ.get("opt_dir")
 pretrained_s2G = os.environ.get("pretrained_s2G")
 s2config_path = os.environ.get("s2config_path")
 
-if os.path.exists(pretrained_s2G):...
+if os.path.exists(pretrained_s2G):
-else:raise FileNotFoundError(pretrained_s2G)
+    ...
+else:
+    raise FileNotFoundError(pretrained_s2G)
 # version=os.environ.get("version","v2")
 size = os.path.getsize(pretrained_s2G)
 if size < 82978 * 1024:
@@ -24,24 +34,14 @@ elif size < 700 * 1024 * 1024:
     version = "v2"
 else:
     version = "v3"
-import torch
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
-import math, traceback
-import multiprocessing
-import sys, pdb
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
-from random import shuffle
-import torch.multiprocessing as mp
-from glob import glob
-from tqdm import tqdm
-import logging, librosa, utils
 if version != "v3":
     from module.models import SynthesizerTrn
 else:
     from module.models import SynthesizerTrnV3 as SynthesizerTrn
-from tools.my_utils import clean_path
 logging.getLogger("numba").setLevel(logging.WARNING)
 # from config import pretrained_s2G
 

webui.py

@@ -1,15 +1,45 @@
-import os,sys
+import json
-if len(sys.argv)==1:sys.argv.append('v2')
+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")
@@ -17,7 +47,8 @@ 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
+        if (name == "jieba.cache"):
+            continue
         path = "%s/%s" % (tmp, name)
         delete = os.remove if os.path.isfile(path) else shutil.rmtree
         try:
@@ -25,13 +56,12 @@ if(os.path.exists(tmp)):
         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]
+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"] = ""
@@ -45,28 +75,17 @@ 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
 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:...
+except:
-import gradio as gr
+    ...
 n_cpu = cpu_count()
 
 ngpu = torch.cuda.device_count()
@@ -92,6 +111,7 @@ if torch.cuda.is_available() or ngpu != 0:
 #     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
@@ -139,23 +159,32 @@ def set_default():
     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
+        if (int(input)not in set_gpu_numbers):
-    except:return input
+            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)))
+        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"]
 
@@ -170,31 +199,41 @@ if _:
 
 _ = [[], []]
 for i in range(3):
-    if os.path.exists(pretrained_gpt_name[i]):_[0].append(pretrained_gpt_name[i])
+    if os.path.exists(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
+        _[0].append(pretrained_gpt_name[i])
-    if os.path.exists(pretrained_sovits_name[i]):_[-1].append(pretrained_sovits_name[i])
+    else:
-    else:_[-1].append("")
+        _[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))
+            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)
 
+
 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)
@@ -202,16 +241,19 @@ 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"}
 
+
 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)
@@ -231,7 +273,10 @@ def kill_proc_tree(pid, including_parent=True):
         except OSError:
             pass
 
+
 system = platform.system()
+
+
 def kill_process(pid, process_name=""):
     if (system == "Windows"):
         cmd = "taskkill /t /f /pid %s" % pid
@@ -241,6 +286,7 @@ def kill_process(pid, process_name=""):
         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")
@@ -263,7 +309,10 @@ 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:
@@ -278,7 +327,10 @@ def change_label(path_list):
         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:
@@ -291,14 +343,17 @@ def change_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
+    # 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:
@@ -318,9 +373,10 @@ def change_tts_inference(bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits
         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:
@@ -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:
@@ -352,7 +409,10 @@ 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):
@@ -370,6 +430,7 @@ def open_denoise(denoise_inp_dir, 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:
@@ -377,8 +438,11 @@ 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
 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):
@@ -409,7 +473,8 @@ def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_s
         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))
+        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:
@@ -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:
@@ -430,8 +496,11 @@ 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
 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):
@@ -462,7 +531,8 @@ def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_
         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))
+        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}
@@ -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:
@@ -481,8 +552,11 @@ def close1Bb():
         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)
@@ -491,8 +565,10 @@ def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_k
     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
+    if os.path.isfile(inp):
-    elif os.path.isdir(inp):pass
+        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
@@ -510,6 +586,7 @@ def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_k
     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 != []):
@@ -521,8 +598,11 @@ def close_slice():
         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)
@@ -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 != []:
@@ -585,8 +666,11 @@ def close1a():
         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)
@@ -625,6 +709,7 @@ def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
     else:
         yield process_info(process_name_1b, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
 
+
 def close1b():
     global ps1b
     if (ps1b != []):
@@ -636,8 +721,11 @@ def close1b():
         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)
@@ -685,6 +773,7 @@ def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
     else:
         yield process_info(process_name_1c, "occupy"), {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
 
+
 def close1c():
     global ps1c
     if (ps1c != []):
@@ -696,8 +785,11 @@ def close1c():
         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)
@@ -707,7 +799,7 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
     if (ps1abc == []):
         opt_dir = "%s/%s" % (exp_root, exp_name)
         try:
-            #############################1a
+            # 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 = {
@@ -734,7 +826,8 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
                     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)
@@ -747,7 +840,7 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
                 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
+            # 1b
             config = {
                 "inp_text": inp_text,
                 "inp_wav_dir": inp_wav_dir,
@@ -771,10 +864,11 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
                 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
+            # 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 = {
@@ -800,7 +894,8 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
                     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):
@@ -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 != []):
@@ -831,11 +927,13 @@ def close1abc():
         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] !='':...
+    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()
@@ -852,23 +950,25 @@ def switch_version(version_):
         {'__type__': 'update', "interactive": False if version == "v3" else True, "value": 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
     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=
+        value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.") + "<br>" + 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.")
-            i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.") + "<br>" + 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=
+        value=i18n("中文教程文档") + ": " + "https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e"
-            i18n("中文教程文档") + ": " + "https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e"
     )
 
     with gr.Tabs():
@@ -931,8 +1031,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 
                 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:
@@ -1121,7 +1223,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 
             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
         server_name="0.0.0.0",