mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-10 18:19:52 +08:00
2081 lines
80 KiB
Python
2081 lines
80 KiB
Python
import os
|
||
import sys
|
||
|
||
if len(sys.argv) == 1:
|
||
sys.argv.append("v2")
|
||
version = "v1" if sys.argv[1] == "v1" else "v4"
|
||
os.environ["version"] = version
|
||
now_dir = os.getcwd()
|
||
sys.path.insert(0, now_dir)
|
||
import warnings
|
||
|
||
warnings.filterwarnings("ignore")
|
||
import json
|
||
import platform
|
||
import re
|
||
import shutil
|
||
import signal
|
||
|
||
import psutil
|
||
import torch
|
||
import yaml
|
||
|
||
os.environ["TORCH_DISTRIBUTED_DEBUG"] = "INFO"
|
||
torch.manual_seed(233333)
|
||
tmp = os.path.join(now_dir, "TEMP")
|
||
os.makedirs(tmp, exist_ok=True)
|
||
os.environ["TEMP"] = tmp
|
||
if os.path.exists(tmp):
|
||
for name in os.listdir(tmp):
|
||
if name == "jieba.cache":
|
||
continue
|
||
path = "%s/%s" % (tmp, name)
|
||
delete = os.remove if os.path.isfile(path) else shutil.rmtree
|
||
try:
|
||
delete(path)
|
||
except Exception as e:
|
||
print(str(e))
|
||
pass
|
||
import site
|
||
import traceback
|
||
|
||
site_packages_roots = []
|
||
for path in site.getsitepackages():
|
||
if "packages" in path:
|
||
site_packages_roots.append(path)
|
||
if site_packages_roots == []:
|
||
site_packages_roots = ["%s/runtime/Lib/site-packages" % now_dir]
|
||
# os.environ["OPENBLAS_NUM_THREADS"] = "4"
|
||
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
|
||
os.environ["all_proxy"] = ""
|
||
for site_packages_root in site_packages_roots:
|
||
if os.path.exists(site_packages_root):
|
||
try:
|
||
with open("%s/users.pth" % (site_packages_root), "w") as f:
|
||
f.write(
|
||
# "%s\n%s/runtime\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
|
||
"%s\n%s/GPT_SoVITS/BigVGAN\n%s/tools\n%s/tools/asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
|
||
% (now_dir, now_dir, now_dir, now_dir, now_dir, now_dir)
|
||
)
|
||
break
|
||
except PermissionError:
|
||
traceback.print_exc()
|
||
import shutil
|
||
import subprocess
|
||
from subprocess import Popen
|
||
|
||
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.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 multiprocessing import cpu_count
|
||
|
||
from tools.my_utils import check_details, check_for_existance
|
||
|
||
# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
|
||
try:
|
||
import gradio.analytics as analytics
|
||
|
||
analytics.version_check = lambda: None
|
||
except:
|
||
...
|
||
import gradio as gr
|
||
|
||
n_cpu = cpu_count()
|
||
|
||
ngpu = torch.cuda.device_count()
|
||
gpu_infos = []
|
||
mem = []
|
||
if_gpu_ok = False
|
||
|
||
# 判断是否有能用来训练和加速推理的N卡
|
||
ok_gpu_keywords = {
|
||
"10",
|
||
"16",
|
||
"20",
|
||
"30",
|
||
"40",
|
||
"A2",
|
||
"A3",
|
||
"A4",
|
||
"P4",
|
||
"A50",
|
||
"500",
|
||
"A60",
|
||
"70",
|
||
"80",
|
||
"90",
|
||
"M4",
|
||
"T4",
|
||
"TITAN",
|
||
"L4",
|
||
"4060",
|
||
"H",
|
||
"600",
|
||
"506",
|
||
"507",
|
||
"508",
|
||
"509",
|
||
}
|
||
set_gpu_numbers = set()
|
||
if torch.cuda.is_available() or ngpu != 0:
|
||
for i in range(ngpu):
|
||
gpu_name = torch.cuda.get_device_name(i)
|
||
if any(value in gpu_name.upper() for value in ok_gpu_keywords):
|
||
# A10#A100#V100#A40#P40#M40#K80#A4500
|
||
if_gpu_ok = True # 至少有一张能用的N卡
|
||
gpu_infos.append("%s\t%s" % (i, gpu_name))
|
||
set_gpu_numbers.add(i)
|
||
mem.append(int(torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4))
|
||
# # 判断是否支持mps加速
|
||
# if torch.backends.mps.is_available():
|
||
# if_gpu_ok = True
|
||
# gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
|
||
# mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
|
||
|
||
|
||
v3v4set={"v3","v4"}
|
||
def set_default():
|
||
global \
|
||
default_batch_size, \
|
||
default_max_batch_size, \
|
||
gpu_info, \
|
||
default_sovits_epoch, \
|
||
default_sovits_save_every_epoch, \
|
||
max_sovits_epoch, \
|
||
max_sovits_save_every_epoch, \
|
||
default_batch_size_s1, \
|
||
if_force_ckpt
|
||
if_force_ckpt = False
|
||
if if_gpu_ok and len(gpu_infos) > 0:
|
||
gpu_info = "\n".join(gpu_infos)
|
||
minmem = min(mem)
|
||
default_batch_size = minmem // 2 if version not in v3v4set 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 not in v3v4set:
|
||
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 = 20 # 40 # 3
|
||
max_sovits_save_every_epoch = 10 # 10 # 3
|
||
|
||
default_batch_size = max(1, default_batch_size)
|
||
default_batch_size_s1 = max(1, default_batch_size_s1)
|
||
default_max_batch_size = default_batch_size * 3
|
||
|
||
|
||
set_default()
|
||
|
||
gpus = "-".join([i[0] for i in gpu_infos])
|
||
default_gpu_numbers = str(sorted(list(set_gpu_numbers))[0])
|
||
|
||
|
||
def fix_gpu_number(input): # 将越界的number强制改到界内
|
||
try:
|
||
if int(input) not in set_gpu_numbers:
|
||
return default_gpu_numbers
|
||
except:
|
||
return input
|
||
return input
|
||
|
||
|
||
def fix_gpu_numbers(inputs):
|
||
output = []
|
||
try:
|
||
for input in inputs.split(","):
|
||
output.append(str(fix_gpu_number(input)))
|
||
return ",".join(output)
|
||
except:
|
||
return inputs
|
||
|
||
|
||
pretrained_sovits_name = [
|
||
"GPT_SoVITS/pretrained_models/s2G488k.pth",
|
||
"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
|
||
"GPT_SoVITS/pretrained_models/s2Gv3.pth",
|
||
"GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.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",
|
||
"GPT_SoVITS/pretrained_models/s1v3.ckpt",
|
||
]
|
||
|
||
pretrained_model_list = (
|
||
pretrained_sovits_name[int(version[-1]) - 1],
|
||
pretrained_sovits_name[int(version[-1]) - 1].replace("s2G", "s2D"),
|
||
pretrained_gpt_name[int(version[-1]) - 1],
|
||
"GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
|
||
"GPT_SoVITS/pretrained_models/chinese-hubert-base",
|
||
)
|
||
|
||
_ = ""
|
||
for i in pretrained_model_list:
|
||
if "s2Dv3" not in i and os.path.exists(i) == False:
|
||
if "s2Dv4" in i:
|
||
continue
|
||
_ += f"\n {i}"
|
||
if os.path.exists(pretrained_sovits_name[3]) == False:
|
||
_ += f"\n {pretrained_sovits_name[3]}"
|
||
if _:
|
||
print("warning: ", i18n("以下模型不存在:") + _)
|
||
|
||
_ = [[], []]
|
||
for i in range(4):
|
||
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", "SoVITS_weights_v4"]
|
||
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
|
||
for root in SoVITS_weight_root + GPT_weight_root:
|
||
os.makedirs(root, exist_ok=True)
|
||
|
||
|
||
def get_weights_names():
|
||
SoVITS_names = [name for name in pretrained_sovits_name if name != ""]
|
||
for path in SoVITS_weight_root:
|
||
for name in os.listdir(path):
|
||
if name.endswith(".pth"):
|
||
SoVITS_names.append("%s/%s" % (path, name))
|
||
GPT_names = [name for name in pretrained_gpt_name if name != ""]
|
||
for path in GPT_weight_root:
|
||
for name in os.listdir(path):
|
||
if name.endswith(".ckpt"):
|
||
GPT_names.append("%s/%s" % (path, name))
|
||
return SoVITS_names, GPT_names
|
||
|
||
|
||
SoVITS_names, GPT_names = get_weights_names()
|
||
for path in SoVITS_weight_root + GPT_weight_root:
|
||
os.makedirs(path, exist_ok=True)
|
||
|
||
|
||
def custom_sort_key(s):
|
||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||
parts = re.split("(\d+)", s)
|
||
# 将数字部分转换为整数，非数字部分保持不变
|
||
parts = [int(part) if part.isdigit() else part for part in parts]
|
||
return parts
|
||
|
||
|
||
def change_choices():
|
||
SoVITS_names, GPT_names = get_weights_names()
|
||
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
|
||
"choices": sorted(GPT_names, key=custom_sort_key),
|
||
"__type__": "update",
|
||
}
|
||
|
||
|
||
p_label = None
|
||
p_uvr5 = None
|
||
p_asr = None
|
||
p_denoise = None
|
||
p_tts_inference = None
|
||
|
||
|
||
def kill_proc_tree(pid, including_parent=True):
|
||
try:
|
||
parent = psutil.Process(pid)
|
||
except psutil.NoSuchProcess:
|
||
# Process already terminated
|
||
return
|
||
|
||
children = parent.children(recursive=True)
|
||
for child in children:
|
||
try:
|
||
os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
|
||
except OSError:
|
||
pass
|
||
if including_parent:
|
||
try:
|
||
os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
|
||
except OSError:
|
||
pass
|
||
|
||
|
||
system = platform.system()
|
||
|
||
|
||
def kill_process(pid, process_name=""):
|
||
if system == "Windows":
|
||
cmd = "taskkill /t /f /pid %s" % pid
|
||
# os.system(cmd)
|
||
subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
|
||
else:
|
||
kill_proc_tree(pid)
|
||
print(process_name + i18n("进程已终止"))
|
||
|
||
|
||
def process_info(process_name="", indicator=""):
|
||
if indicator == "opened":
|
||
return process_name + i18n("已开启")
|
||
elif indicator == "open":
|
||
return i18n("开启") + process_name
|
||
elif indicator == "closed":
|
||
return process_name + i18n("已关闭")
|
||
elif indicator == "close":
|
||
return i18n("关闭") + process_name
|
||
elif indicator == "running":
|
||
return process_name + i18n("运行中")
|
||
elif indicator == "occupy":
|
||
return process_name + i18n("占用中") + "," + i18n("需先终止才能开启下一次任务")
|
||
elif indicator == "finish":
|
||
return process_name + i18n("已完成")
|
||
elif indicator == "failed":
|
||
return process_name + i18n("失败")
|
||
elif indicator == "info":
|
||
return process_name + i18n("进程输出信息")
|
||
else:
|
||
return process_name
|
||
|
||
|
||
process_name_subfix = i18n("音频标注WebUI")
|
||
|
||
|
||
def change_label(path_list):
|
||
global p_label
|
||
if p_label is None:
|
||
check_for_existance([path_list])
|
||
path_list = my_utils.clean_path(path_list)
|
||
cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s' % (
|
||
python_exec,
|
||
path_list,
|
||
webui_port_subfix,
|
||
is_share,
|
||
)
|
||
yield (
|
||
process_info(process_name_subfix, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
print(cmd)
|
||
p_label = Popen(cmd, shell=True)
|
||
else:
|
||
kill_process(p_label.pid, process_name_subfix)
|
||
p_label = None
|
||
yield (
|
||
process_info(process_name_subfix, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
process_name_uvr5 = i18n("人声分离WebUI")
|
||
|
||
|
||
def change_uvr5():
|
||
global p_uvr5
|
||
if p_uvr5 is None:
|
||
cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s' % (python_exec, infer_device, is_half, webui_port_uvr5, is_share)
|
||
yield (
|
||
process_info(process_name_uvr5, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
print(cmd)
|
||
p_uvr5 = Popen(cmd, shell=True)
|
||
else:
|
||
kill_process(p_uvr5.pid, process_name_uvr5)
|
||
p_uvr5 = None
|
||
yield (
|
||
process_info(process_name_uvr5, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
process_name_tts = i18n("TTS推理WebUI")
|
||
|
||
|
||
def change_tts_inference(bert_path, cnhubert_base_path, gpu_number, gpt_path, sovits_path, batched_infer_enabled):
|
||
global p_tts_inference
|
||
if batched_infer_enabled:
|
||
cmd = '"%s" GPT_SoVITS/inference_webui_fast.py "%s"' % (python_exec, language)
|
||
else:
|
||
cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"' % (python_exec, language)
|
||
# #####v3暂不支持加速推理
|
||
# if version=="v3":
|
||
# cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language)
|
||
if p_tts_inference is None:
|
||
os.environ["gpt_path"] = gpt_path if "/" in gpt_path else "%s/%s" % (GPT_weight_root, gpt_path)
|
||
os.environ["sovits_path"] = sovits_path if "/" in sovits_path else "%s/%s" % (SoVITS_weight_root, sovits_path)
|
||
os.environ["cnhubert_base_path"] = cnhubert_base_path
|
||
os.environ["bert_path"] = bert_path
|
||
os.environ["_CUDA_VISIBLE_DEVICES"] = fix_gpu_number(gpu_number)
|
||
os.environ["is_half"] = str(is_half)
|
||
os.environ["infer_ttswebui"] = str(webui_port_infer_tts)
|
||
os.environ["is_share"] = str(is_share)
|
||
yield (
|
||
process_info(process_name_tts, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
print(cmd)
|
||
p_tts_inference = Popen(cmd, shell=True)
|
||
else:
|
||
kill_process(p_tts_inference.pid, process_name_tts)
|
||
p_tts_inference = None
|
||
yield (
|
||
process_info(process_name_tts, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
from tools.asr.config import asr_dict
|
||
|
||
process_name_asr = i18n("语音识别")
|
||
|
||
|
||
def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision):
|
||
global p_asr
|
||
if p_asr is None:
|
||
asr_inp_dir = my_utils.clean_path(asr_inp_dir)
|
||
asr_opt_dir = my_utils.clean_path(asr_opt_dir)
|
||
check_for_existance([asr_inp_dir])
|
||
cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
|
||
cmd += f' -i "{asr_inp_dir}"'
|
||
cmd += f' -o "{asr_opt_dir}"'
|
||
cmd += f" -s {asr_model_size}"
|
||
cmd += f" -l {asr_lang}"
|
||
cmd += f" -p {asr_precision}"
|
||
output_file_name = os.path.basename(asr_inp_dir)
|
||
output_folder = asr_opt_dir or "output/asr_opt"
|
||
output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")
|
||
yield (
|
||
process_info(process_name_asr, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
print(cmd)
|
||
p_asr = Popen(cmd, shell=True)
|
||
p_asr.wait()
|
||
p_asr = None
|
||
yield (
|
||
process_info(process_name_asr, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "value": output_file_path},
|
||
{"__type__": "update", "value": output_file_path},
|
||
{"__type__": "update", "value": asr_inp_dir},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_asr, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
|
||
|
||
def close_asr():
|
||
global p_asr
|
||
if p_asr is not None:
|
||
kill_process(p_asr.pid, process_name_asr)
|
||
p_asr = None
|
||
return (
|
||
process_info(process_name_asr, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
process_name_denoise = i18n("语音降噪")
|
||
|
||
|
||
def open_denoise(denoise_inp_dir, denoise_opt_dir):
|
||
global p_denoise
|
||
if p_denoise == None:
|
||
denoise_inp_dir = my_utils.clean_path(denoise_inp_dir)
|
||
denoise_opt_dir = my_utils.clean_path(denoise_opt_dir)
|
||
check_for_existance([denoise_inp_dir])
|
||
cmd = '"%s" tools/cmd-denoise.py -i "%s" -o "%s" -p %s' % (
|
||
python_exec,
|
||
denoise_inp_dir,
|
||
denoise_opt_dir,
|
||
"float16" if is_half == True else "float32",
|
||
)
|
||
|
||
yield (
|
||
process_info(process_name_denoise, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
print(cmd)
|
||
p_denoise = Popen(cmd, shell=True)
|
||
p_denoise.wait()
|
||
p_denoise = None
|
||
yield (
|
||
process_info(process_name_denoise, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "value": denoise_opt_dir},
|
||
{"__type__": "update", "value": denoise_opt_dir},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_denoise, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
|
||
|
||
def close_denoise():
|
||
global p_denoise
|
||
if p_denoise is not None:
|
||
kill_process(p_denoise.pid, process_name_denoise)
|
||
p_denoise = None
|
||
return (
|
||
process_info(process_name_denoise, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
p_train_SoVITS = None
|
||
process_name_sovits = i18n("SoVITS训练")
|
||
|
||
def open1Ba(
|
||
batch_size,
|
||
total_epoch,
|
||
exp_name,
|
||
text_low_lr_rate,
|
||
if_save_latest,
|
||
if_save_every_weights,
|
||
save_every_epoch,
|
||
gpu_numbers1Ba,
|
||
pretrained_s2G,
|
||
pretrained_s2D,
|
||
if_grad_ckpt,
|
||
lora_rank,
|
||
):
|
||
global p_train_SoVITS
|
||
if p_train_SoVITS == None:
|
||
with open("GPT_SoVITS/configs/s2.json") as f:
|
||
data = f.read()
|
||
data = json.loads(data)
|
||
s2_dir = "%s/%s" % (exp_root, exp_name)
|
||
os.makedirs("%s/logs_s2_%s" % (s2_dir, version), exist_ok=True)
|
||
if check_for_existance([s2_dir], is_train=True):
|
||
check_details([s2_dir], is_train=True)
|
||
if is_half == False:
|
||
data["train"]["fp16_run"] = False
|
||
batch_size = max(1, batch_size // 2)
|
||
data["train"]["batch_size"] = batch_size
|
||
data["train"]["epochs"] = total_epoch
|
||
data["train"]["text_low_lr_rate"] = text_low_lr_rate
|
||
data["train"]["pretrained_s2G"] = pretrained_s2G
|
||
data["train"]["pretrained_s2D"] = pretrained_s2D
|
||
data["train"]["if_save_latest"] = if_save_latest
|
||
data["train"]["if_save_every_weights"] = if_save_every_weights
|
||
data["train"]["save_every_epoch"] = save_every_epoch
|
||
data["train"]["gpu_numbers"] = gpu_numbers1Ba
|
||
data["train"]["grad_ckpt"] = if_grad_ckpt
|
||
data["train"]["lora_rank"] = lora_rank
|
||
data["model"]["version"] = version
|
||
data["data"]["exp_dir"] = data["s2_ckpt_dir"] = s2_dir
|
||
data["save_weight_dir"] = SoVITS_weight_root[int(version[-1]) - 1]
|
||
data["name"] = exp_name
|
||
data["version"] = version
|
||
tmp_config_path = "%s/tmp_s2.json" % tmp
|
||
with open(tmp_config_path, "w") as f:
|
||
f.write(json.dumps(data))
|
||
if version in ["v1", "v2"]:
|
||
cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"' % (python_exec, tmp_config_path)
|
||
else:
|
||
cmd = '"%s" GPT_SoVITS/s2_train_v3_lora.py --config "%s"' % (python_exec, tmp_config_path)
|
||
yield (
|
||
process_info(process_name_sovits, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"}
|
||
)
|
||
print(cmd)
|
||
p_train_SoVITS = Popen(cmd, shell=True)
|
||
p_train_SoVITS.wait()
|
||
p_train_SoVITS = None
|
||
SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
|
||
yield (
|
||
process_info(process_name_sovits, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},SoVITS_dropdown_update,GPT_dropdown_update
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_sovits, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"}
|
||
)
|
||
|
||
|
||
def close1Ba():
|
||
global p_train_SoVITS
|
||
if p_train_SoVITS is not None:
|
||
kill_process(p_train_SoVITS.pid, process_name_sovits)
|
||
p_train_SoVITS = None
|
||
return (
|
||
process_info(process_name_sovits, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
p_train_GPT = None
|
||
process_name_gpt = i18n("GPT训练")
|
||
|
||
|
||
def open1Bb(
|
||
batch_size,
|
||
total_epoch,
|
||
exp_name,
|
||
if_dpo,
|
||
if_save_latest,
|
||
if_save_every_weights,
|
||
save_every_epoch,
|
||
gpu_numbers,
|
||
pretrained_s1,
|
||
):
|
||
global p_train_GPT
|
||
if p_train_GPT == None:
|
||
with open(
|
||
"GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml"
|
||
) as f:
|
||
data = f.read()
|
||
data = yaml.load(data, Loader=yaml.FullLoader)
|
||
s1_dir = "%s/%s" % (exp_root, exp_name)
|
||
os.makedirs("%s/logs_s1" % (s1_dir), exist_ok=True)
|
||
if check_for_existance([s1_dir], is_train=True):
|
||
check_details([s1_dir], is_train=True)
|
||
if is_half == False:
|
||
data["train"]["precision"] = "32"
|
||
batch_size = max(1, batch_size // 2)
|
||
data["train"]["batch_size"] = batch_size
|
||
data["train"]["epochs"] = total_epoch
|
||
data["pretrained_s1"] = pretrained_s1
|
||
data["train"]["save_every_n_epoch"] = save_every_epoch
|
||
data["train"]["if_save_every_weights"] = if_save_every_weights
|
||
data["train"]["if_save_latest"] = if_save_latest
|
||
data["train"]["if_dpo"] = if_dpo
|
||
data["train"]["half_weights_save_dir"] = GPT_weight_root[int(version[-1]) - 1]
|
||
data["train"]["exp_name"] = exp_name
|
||
data["train_semantic_path"] = "%s/6-name2semantic.tsv" % s1_dir
|
||
data["train_phoneme_path"] = "%s/2-name2text.txt" % s1_dir
|
||
data["output_dir"] = "%s/logs_s1_%s" % (s1_dir, version)
|
||
# data["version"]=version
|
||
|
||
os.environ["_CUDA_VISIBLE_DEVICES"] = fix_gpu_numbers(gpu_numbers.replace("-", ","))
|
||
os.environ["hz"] = "25hz"
|
||
tmp_config_path = "%s/tmp_s1.yaml" % tmp
|
||
with open(tmp_config_path, "w") as f:
|
||
f.write(yaml.dump(data, default_flow_style=False))
|
||
# cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
|
||
cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" ' % (python_exec, tmp_config_path)
|
||
yield (
|
||
process_info(process_name_gpt, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"}
|
||
)
|
||
print(cmd)
|
||
p_train_GPT = Popen(cmd, shell=True)
|
||
p_train_GPT.wait()
|
||
p_train_GPT = None
|
||
SoVITS_dropdown_update, GPT_dropdown_update = change_choices()
|
||
yield (
|
||
process_info(process_name_gpt, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},SoVITS_dropdown_update,GPT_dropdown_update
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_gpt, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"}
|
||
)
|
||
|
||
|
||
def close1Bb():
|
||
global p_train_GPT
|
||
if p_train_GPT is not None:
|
||
kill_process(p_train_GPT.pid, process_name_gpt)
|
||
p_train_GPT = None
|
||
return (
|
||
process_info(process_name_gpt, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
ps_slice = []
|
||
process_name_slice = i18n("语音切分")
|
||
|
||
|
||
def open_slice(inp_list, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_parts):
|
||
if not inp_list or len(inp_list) == 0:
|
||
yield (
|
||
i18n("未选择任何文件"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
return
|
||
global ps_slice
|
||
opt_root = my_utils.clean_path(opt_root)
|
||
os.makedirs(opt_root, exist_ok=True)
|
||
|
||
for idx, inp in enumerate(inp_list):
|
||
inp = my_utils.clean_path(inp)
|
||
if not os.path.isfile(inp):
|
||
yield (
|
||
f"{i18n('文件')} {inp} {i18n('不存在')}",
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
continue
|
||
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,
|
||
"0",
|
||
"1",
|
||
)
|
||
print(f"执行命令: {cmd}")
|
||
p = Popen(cmd, shell=True)
|
||
ps_slice.append(p)
|
||
|
||
yield (
|
||
process_info(process_name_slice, "opened"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
{"__type__": "update"},
|
||
)
|
||
|
||
for p in ps_slice:
|
||
p.wait()
|
||
ps_slice = []
|
||
|
||
yield (
|
||
process_info(process_name_slice, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "value": opt_root},
|
||
{"__type__": "update", "value": opt_root},
|
||
{"__type__": "update", "value": opt_root},
|
||
)
|
||
|
||
|
||
def close_slice():
|
||
global ps_slice
|
||
if ps_slice != []:
|
||
for p_slice in ps_slice:
|
||
try:
|
||
kill_process(p_slice.pid, process_name_slice)
|
||
except:
|
||
traceback.print_exc()
|
||
ps_slice = []
|
||
return (
|
||
process_info(process_name_slice, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
ps1a = []
|
||
process_name_1a = i18n("文本分词与特征提取")
|
||
|
||
|
||
def open1a(inp_text, inp_wav_dir, exp_name, gpu_numbers, bert_pretrained_dir):
|
||
global ps1a
|
||
inp_text = my_utils.clean_path(inp_text)
|
||
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
|
||
if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
|
||
check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
|
||
if ps1a == []:
|
||
opt_dir = "%s/%s" % (exp_root, exp_name)
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"inp_wav_dir": inp_wav_dir,
|
||
"exp_name": exp_name,
|
||
"opt_dir": opt_dir,
|
||
"bert_pretrained_dir": bert_pretrained_dir,
|
||
}
|
||
gpu_names = gpu_numbers.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
"is_half": str(is_half),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1a.append(p)
|
||
yield (
|
||
process_info(process_name_1a, "running"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1a:
|
||
p.wait()
|
||
opt = []
|
||
for i_part in range(all_parts):
|
||
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
|
||
with open(txt_path, "r", encoding="utf8") as f:
|
||
opt += f.read().strip("\n").split("\n")
|
||
os.remove(txt_path)
|
||
path_text = "%s/2-name2text.txt" % opt_dir
|
||
with open(path_text, "w", encoding="utf8") as f:
|
||
f.write("\n".join(opt) + "\n")
|
||
ps1a = []
|
||
if len("".join(opt)) > 0:
|
||
yield (
|
||
process_info(process_name_1a, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_1a, "failed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_1a, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
|
||
|
||
def close1a():
|
||
global ps1a
|
||
if ps1a != []:
|
||
for p1a in ps1a:
|
||
try:
|
||
kill_process(p1a.pid, process_name_1a)
|
||
except:
|
||
traceback.print_exc()
|
||
ps1a = []
|
||
return (
|
||
process_info(process_name_1a, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
ps1b = []
|
||
process_name_1b = i18n("语音自监督特征提取")
|
||
|
||
|
||
def open1b(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
|
||
global ps1b
|
||
inp_text = my_utils.clean_path(inp_text)
|
||
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
|
||
if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
|
||
check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
|
||
if ps1b == []:
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"inp_wav_dir": inp_wav_dir,
|
||
"exp_name": exp_name,
|
||
"opt_dir": "%s/%s" % (exp_root, exp_name),
|
||
"cnhubert_base_dir": ssl_pretrained_dir,
|
||
"is_half": str(is_half),
|
||
}
|
||
gpu_names = gpu_numbers.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1b.append(p)
|
||
yield (
|
||
process_info(process_name_1b, "running"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1b:
|
||
p.wait()
|
||
ps1b = []
|
||
yield (
|
||
process_info(process_name_1b, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_1b, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
|
||
|
||
def close1b():
|
||
global ps1b
|
||
if ps1b != []:
|
||
for p1b in ps1b:
|
||
try:
|
||
kill_process(p1b.pid, process_name_1b)
|
||
except:
|
||
traceback.print_exc()
|
||
ps1b = []
|
||
return (
|
||
process_info(process_name_1b, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
ps1c = []
|
||
process_name_1c = i18n("语义Token提取")
|
||
|
||
|
||
def open1c(inp_text, exp_name, gpu_numbers, pretrained_s2G_path):
|
||
global ps1c
|
||
inp_text = my_utils.clean_path(inp_text)
|
||
if check_for_existance([inp_text, ""], is_dataset_processing=True):
|
||
check_details([inp_text, ""], is_dataset_processing=True)
|
||
if ps1c == []:
|
||
opt_dir = "%s/%s" % (exp_root, exp_name)
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"exp_name": exp_name,
|
||
"opt_dir": opt_dir,
|
||
"pretrained_s2G": pretrained_s2G_path,
|
||
"s2config_path": "GPT_SoVITS/configs/s2.json",
|
||
"is_half": str(is_half),
|
||
}
|
||
gpu_names = gpu_numbers.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1c.append(p)
|
||
yield (
|
||
process_info(process_name_1c, "running"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1c:
|
||
p.wait()
|
||
opt = ["item_name\tsemantic_audio"]
|
||
path_semantic = "%s/6-name2semantic.tsv" % opt_dir
|
||
for i_part in range(all_parts):
|
||
semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
|
||
with open(semantic_path, "r", encoding="utf8") as f:
|
||
opt += f.read().strip("\n").split("\n")
|
||
os.remove(semantic_path)
|
||
with open(path_semantic, "w", encoding="utf8") as f:
|
||
f.write("\n".join(opt) + "\n")
|
||
ps1c = []
|
||
yield (
|
||
process_info(process_name_1c, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_1c, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
|
||
|
||
def close1c():
|
||
global ps1c
|
||
if ps1c != []:
|
||
for p1c in ps1c:
|
||
try:
|
||
kill_process(p1c.pid, process_name_1c)
|
||
except:
|
||
traceback.print_exc()
|
||
ps1c = []
|
||
return (
|
||
process_info(process_name_1c, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
|
||
ps1abc = []
|
||
process_name_1abc = i18n("训练集格式化一键三连")
|
||
|
||
|
||
def open1abc(
|
||
inp_text,
|
||
inp_wav_dir,
|
||
exp_name,
|
||
gpu_numbers1a,
|
||
gpu_numbers1Ba,
|
||
gpu_numbers1c,
|
||
bert_pretrained_dir,
|
||
ssl_pretrained_dir,
|
||
pretrained_s2G_path,
|
||
):
|
||
global ps1abc
|
||
inp_text = my_utils.clean_path(inp_text)
|
||
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
|
||
if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
|
||
check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
|
||
if ps1abc == []:
|
||
opt_dir = "%s/%s" % (exp_root, exp_name)
|
||
try:
|
||
#############################1a
|
||
path_text = "%s/2-name2text.txt" % opt_dir
|
||
if os.path.exists(path_text) == False or (
|
||
os.path.exists(path_text) == True
|
||
and len(open(path_text, "r", encoding="utf8").read().strip("\n").split("\n")) < 2
|
||
):
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"inp_wav_dir": inp_wav_dir,
|
||
"exp_name": exp_name,
|
||
"opt_dir": opt_dir,
|
||
"bert_pretrained_dir": bert_pretrained_dir,
|
||
"is_half": str(is_half),
|
||
}
|
||
gpu_names = gpu_numbers1a.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1abc.append(p)
|
||
yield (
|
||
i18n("进度") + ": 1A-Doing",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1abc:
|
||
p.wait()
|
||
|
||
opt = []
|
||
for i_part in range(all_parts): # txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
|
||
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
|
||
with open(txt_path, "r", encoding="utf8") as f:
|
||
opt += f.read().strip("\n").split("\n")
|
||
os.remove(txt_path)
|
||
with open(path_text, "w", encoding="utf8") as f:
|
||
f.write("\n".join(opt) + "\n")
|
||
assert len("".join(opt)) > 0, process_info(process_name_1a, "failed")
|
||
yield (
|
||
i18n("进度") + ": 1A-Done",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
ps1abc = []
|
||
#############################1b
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"inp_wav_dir": inp_wav_dir,
|
||
"exp_name": exp_name,
|
||
"opt_dir": opt_dir,
|
||
"cnhubert_base_dir": ssl_pretrained_dir,
|
||
}
|
||
gpu_names = gpu_numbers1Ba.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1abc.append(p)
|
||
yield (
|
||
i18n("进度") + ": 1A-Done, 1B-Doing",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1abc:
|
||
p.wait()
|
||
yield (
|
||
i18n("进度") + ": 1A-Done, 1B-Done",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
ps1abc = []
|
||
#############################1c
|
||
path_semantic = "%s/6-name2semantic.tsv" % opt_dir
|
||
if os.path.exists(path_semantic) == False or (
|
||
os.path.exists(path_semantic) == True and os.path.getsize(path_semantic) < 31
|
||
):
|
||
config = {
|
||
"inp_text": inp_text,
|
||
"exp_name": exp_name,
|
||
"opt_dir": opt_dir,
|
||
"pretrained_s2G": pretrained_s2G_path,
|
||
"s2config_path": "GPT_SoVITS/configs/s2.json",
|
||
}
|
||
gpu_names = gpu_numbers1c.split("-")
|
||
all_parts = len(gpu_names)
|
||
for i_part in range(all_parts):
|
||
config.update(
|
||
{
|
||
"i_part": str(i_part),
|
||
"all_parts": str(all_parts),
|
||
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
|
||
}
|
||
)
|
||
os.environ.update(config)
|
||
cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py' % python_exec
|
||
print(cmd)
|
||
p = Popen(cmd, shell=True)
|
||
ps1abc.append(p)
|
||
yield (
|
||
i18n("进度") + ": 1A-Done, 1B-Done, 1C-Doing",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
for p in ps1abc:
|
||
p.wait()
|
||
|
||
opt = ["item_name\tsemantic_audio"]
|
||
for i_part in range(all_parts):
|
||
semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
|
||
with open(semantic_path, "r", encoding="utf8") as f:
|
||
opt += f.read().strip("\n").split("\n")
|
||
os.remove(semantic_path)
|
||
with open(path_semantic, "w", encoding="utf8") as f:
|
||
f.write("\n".join(opt) + "\n")
|
||
yield (
|
||
i18n("进度") + ": 1A-Done, 1B-Done, 1C-Done",
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
ps1abc = []
|
||
yield (
|
||
process_info(process_name_1abc, "finish"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
except:
|
||
traceback.print_exc()
|
||
close1abc()
|
||
yield (
|
||
process_info(process_name_1abc, "failed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
else:
|
||
yield (
|
||
process_info(process_name_1abc, "occupy"),
|
||
{"__type__": "update", "visible": False},
|
||
{"__type__": "update", "visible": True},
|
||
)
|
||
|
||
|
||
def close1abc():
|
||
global ps1abc
|
||
if ps1abc != []:
|
||
for p1abc in ps1abc:
|
||
try:
|
||
kill_process(p1abc.pid, process_name_1abc)
|
||
except:
|
||
traceback.print_exc()
|
||
ps1abc = []
|
||
return (
|
||
process_info(process_name_1abc, "closed"),
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
)
|
||
|
||
def switch_version(version_):
|
||
os.environ["version"] = version_
|
||
global version
|
||
version = version_
|
||
if pretrained_sovits_name[int(version[-1]) - 1] != "" and pretrained_gpt_name[int(version[-1]) - 1] != "":
|
||
...
|
||
else:
|
||
gr.Warning(i18n("未下载模型") + ": " + version.upper())
|
||
set_default()
|
||
print(f"{i18n('预训练SoVITS-G模型路径')}: {pretrained_sovits_name[int(version[-1]) - 1]}")
|
||
print(f"{i18n('预训练SoVITS-D模型路径')}: {pretrained_sovits_name[int(version[-1]) - 1].replace('s2G', 's2D')}")
|
||
print(f"{i18n('预训练GPT模型路径')}: {pretrained_gpt_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 not in v3v4set else False},
|
||
{
|
||
"__type__": "update",
|
||
"value": False if not if_force_ckpt else True,
|
||
"interactive": True if not if_force_ckpt else False,
|
||
},
|
||
{"__type__": "update", "interactive": True, "value": False},
|
||
{"__type__": "update", "visible": True if version in v3v4set else False},
|
||
) # {'__type__': 'update', "interactive": False if version in v3v4set else True, "value": False}, \ ####batch infer
|
||
|
||
|
||
if os.path.exists("GPT_SoVITS/text/G2PWModel"):
|
||
...
|
||
else:
|
||
cmd = '"%s" GPT_SoVITS/download.py' % python_exec
|
||
p = Popen(cmd, shell=True)
|
||
p.wait()
|
||
|
||
|
||
def sync(text):
|
||
return {"__type__": "update", "value": text}
|
||
|
||
|
||
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||
with gr.Accordion("使用说明", open=False):
|
||
gr.Markdown(
|
||
value="""
|
||
## 微调训练步骤（简化版）
|
||
0. 填写模型名。
|
||
1. 选择一个或多个音频文件，**勾选`自动开启语音识别`**，然后点击`1.开启语音切分`。`2.开启语音识别`会被自动执行。
|
||
2. **勾选`自动开启SoVITS训练`、`自动开启GPT训练`**，然后点击`4.开启训练集格式化一键三连`。`5.开启SoVITS训练`、`6.开启GPT训练`会被自动执行。
|
||
|
||
## 微调训练步骤
|
||
0. 填写模型名。
|
||
1. 选择一个或多个音频文件，然后点击`1.开启语音切分`。如果已事先完成切分，可直接在`语音切分文件夹路径`里输入对应的路径。
|
||
2. 选择一个ASR模型，然后点击`2.开启语音识别`来生成标注文件。如果已事先准备了标注文件，可直接在`标注文件路径`里输入对应的`.list`文件路径。
|
||
3. 如果需要对标注文件进行修改，可以点击`3.开启音频标注WebUI`，但这一步是非必须的。
|
||
4. 点击`4.开启训练集格式化一键三连`，这一步是为了将数据集整理为特定格式，后续可在`logs`目录里查看。
|
||
5. 根据自己显卡的情况调整batch_size，设置合适的训练轮数，然后点击`5.开启SoVITS训练`，可在终端查看训练进度。
|
||
6. 同样需要调整batch_size，设置合适的训练轮数，然后点击`6.开启GPT训练`，可在终端查看训练进度。如果显存大于12G，且数据集质量好，建议开启DPO。
|
||
|
||
## 推理步骤
|
||
0. 默认是`微调训练`页面，所以需要先切换到`推理`页面。
|
||
1. 在列表里选择之前训练好的GPT模型和SoVITS模型。
|
||
2. 点击`开启TTS推理WebUI`。
|
||
|
||
## 训练新的模型
|
||
1. 切换到`其他工具`页面。
|
||
2. 点击`清空输出目录`。
|
||
3. 切换回`微调训练`页面，按照微调训练步骤从头开始。**记得换一个模型名。**
|
||
|
||
## 免责声明
|
||
本软件以MIT协议开源，作者不对软件具备任何控制力，使用软件者、传播软件导出的声音者自负全责。
|
||
如不认可该条款，则不能使用或引用软件包内任何代码和文件，详见根目录LICENSE。
|
||
|
||
## 快速跳转
|
||
- 中文教程文档: https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e
|
||
- GitHub项目页面: https://github.com/RVC-Boss/GPT-SoVITS
|
||
""")
|
||
|
||
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", "v4"])
|
||
with gr.Row(visible=False):
|
||
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.Tabs():
|
||
with gr.TabItem(i18n("微调训练")):
|
||
# 语音切分工具
|
||
with gr.Row():
|
||
slice_inp_path = gr.Files(label=i18n("选择一个或多个音频文件"), file_types=["audio"])
|
||
slice_opt_root = gr.Textbox(label=i18n("切分后的子音频的输出根目录"), value="output/slicer_opt", visible=False)
|
||
with gr.Row(visible=False):
|
||
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(visible=False):
|
||
_max = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
step=0.05,
|
||
label=i18n("max:归一化后最大值多少"),
|
||
value=0.9,
|
||
interactive=True,
|
||
)
|
||
alpha = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
step=0.05,
|
||
label=i18n("alpha_mix:混多少比例归一化后音频进来"),
|
||
value=0.25,
|
||
interactive=True,
|
||
)
|
||
with gr.Row(visible=False):
|
||
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
|
||
)
|
||
with gr.Row():
|
||
slicer_info = gr.Textbox(label=process_info(process_name_slice, "info"))
|
||
open_slicer_button = gr.Button(
|
||
value="1."+process_info(process_name_slice, "open"), variant="primary", visible=True
|
||
)
|
||
close_slicer_button = gr.Button(
|
||
value="1."+process_info(process_name_slice, "close"), variant="primary", visible=False
|
||
)
|
||
|
||
# 语音降噪工具
|
||
with gr.Row(visible=False):
|
||
with gr.Column(scale=3):
|
||
with gr.Row():
|
||
denoise_input_dir = gr.Textbox(label=i18n("输入文件夹路径"), value="output/slicer_opt")
|
||
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
|
||
)
|
||
|
||
# 语音识别工具
|
||
with gr.Row():
|
||
asr_inp_dir = gr.Textbox(
|
||
label=i18n("语音切分文件夹路径"), value="output/slicer_opt", interactive=True
|
||
)
|
||
asr_opt_dir = gr.Textbox(label=i18n("输出文件夹路径"), value="output/asr_opt", interactive=True, visible=False)
|
||
if_auto_asr = gr.Checkbox(
|
||
label=i18n("自动开启语音识别"),
|
||
value=False,
|
||
interactive=True,
|
||
show_label=True,
|
||
)
|
||
with gr.Row():
|
||
asr_model = gr.Dropdown(
|
||
label=i18n("ASR 模型"),
|
||
choices=list(asr_dict.keys()),
|
||
interactive=True,
|
||
value="达摩 ASR (中文)",
|
||
)
|
||
asr_size = gr.Dropdown(
|
||
label=i18n("ASR 模型尺寸"), choices=["large"], interactive=True, value="large"
|
||
)
|
||
asr_lang = gr.Dropdown(
|
||
label=i18n("ASR 语言设置"), choices=["zh", "yue"], interactive=True, value="zh"
|
||
)
|
||
asr_precision = gr.Dropdown(
|
||
label=i18n("数据类型精度"), choices=["float32"], interactive=True, value="float32"
|
||
)
|
||
with gr.Row():
|
||
asr_info = gr.Textbox(label=process_info(process_name_asr, "info"))
|
||
open_asr_button = gr.Button(
|
||
value="2."+process_info(process_name_asr, "open"), variant="primary", visible=True
|
||
)
|
||
close_asr_button = gr.Button(
|
||
value="2."+process_info(process_name_asr, "close"), variant="primary", visible=False
|
||
)
|
||
|
||
def change_lang_choices(key): # 根据选择的模型修改可选的语言
|
||
return {"__type__": "update", "choices": asr_dict[key]["lang"], "value": asr_dict[key]["lang"][0]}
|
||
|
||
def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸
|
||
return {"__type__": "update", "choices": asr_dict[key]["size"], "value": asr_dict[key]["size"][-1]}
|
||
|
||
def change_precision_choices(key): # 根据选择的模型修改可选的语言
|
||
if key == "Faster Whisper (多语种)":
|
||
if default_batch_size <= 4:
|
||
precision = "int8"
|
||
elif is_half:
|
||
precision = "float16"
|
||
else:
|
||
precision = "float32"
|
||
else:
|
||
precision = "float32"
|
||
return {"__type__": "update", "choices": asr_dict[key]["precision"], "value": precision}
|
||
|
||
asr_model.change(change_lang_choices, [asr_model], [asr_lang])
|
||
asr_model.change(change_size_choices, [asr_model], [asr_size])
|
||
asr_model.change(change_precision_choices, [asr_model], [asr_precision])
|
||
|
||
# 语音文本校对标注工具
|
||
path_list = gr.Textbox(
|
||
label=i18n("标注文件路径 (含文件后缀 *.list)"),
|
||
value="output/asr_opt/slicer_opt.list",
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
label_info = gr.Textbox(label=process_info(process_name_subfix, "info"))
|
||
open_label = gr.Button(value="3."+process_info(process_name_subfix, "open"), variant="primary", visible=True)
|
||
close_label = gr.Button(
|
||
value="3."+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])
|
||
|
||
# 训练集格式化工具
|
||
with gr.Row(visible=False):
|
||
with gr.Row():
|
||
inp_text = gr.Textbox(
|
||
label=i18n("*文本标注文件"),
|
||
value=os.path.join(os.path.dirname(os.path.abspath(__file__)), "output", "asr_opt", "slicer_opt.list"),
|
||
interactive=True,
|
||
scale=10,
|
||
)
|
||
with gr.Row():
|
||
inp_wav_dir = gr.Textbox(
|
||
label=i18n("*训练集音频文件目录"),
|
||
value=os.path.join(os.path.dirname(os.path.abspath(__file__)), "output", "slicer_opt"),
|
||
interactive=True,
|
||
placeholder=i18n(
|
||
"填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。如果留空则使用.list文件里的绝对全路径。"
|
||
),
|
||
scale=10,
|
||
)
|
||
with gr.Row(visible=False):
|
||
with gr.Row():
|
||
gpu_numbers1a = gr.Textbox(
|
||
label=i18n("GPU卡号以-分割，每个卡号一个进程"),
|
||
value="%s-%s" % (gpus, gpus),
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
bert_pretrained_dir = gr.Textbox(
|
||
label=i18n("预训练中文BERT模型路径"),
|
||
value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
|
||
interactive=False,
|
||
lines=2,
|
||
)
|
||
with gr.Row():
|
||
button1a_open = gr.Button(
|
||
value=process_info(process_name_1a, "open"), variant="primary", visible=True
|
||
)
|
||
button1a_close = gr.Button(
|
||
value=process_info(process_name_1a, "close"), variant="primary", visible=False
|
||
)
|
||
with gr.Row():
|
||
info1a = gr.Textbox(label=process_info(process_name_1a, "info"))
|
||
with gr.Row(visible=False):
|
||
with gr.Row():
|
||
gpu_numbers1Ba = gr.Textbox(
|
||
label=i18n("GPU卡号以-分割，每个卡号一个进程"),
|
||
value="%s-%s" % (gpus, gpus),
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
cnhubert_base_dir = gr.Textbox(
|
||
label=i18n("预训练SSL模型路径"),
|
||
value="GPT_SoVITS/pretrained_models/chinese-hubert-base",
|
||
interactive=False,
|
||
lines=2,
|
||
)
|
||
with gr.Row():
|
||
button1b_open = gr.Button(
|
||
value=process_info(process_name_1b, "open"), variant="primary", visible=True
|
||
)
|
||
button1b_close = gr.Button(
|
||
value=process_info(process_name_1b, "close"), variant="primary", visible=False
|
||
)
|
||
with gr.Row():
|
||
info1b = gr.Textbox(label=process_info(process_name_1b, "info"))
|
||
with gr.Row(visible=False):
|
||
with gr.Row():
|
||
gpu_numbers1c = gr.Textbox(
|
||
label=i18n("GPU卡号以-分割，每个卡号一个进程"),
|
||
value="%s-%s" % (gpus, gpus),
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
pretrained_s2G_ = gr.Textbox(
|
||
label=i18n("预训练SoVITS-G模型路径"),
|
||
value=pretrained_sovits_name[int(version[-1]) - 1],
|
||
interactive=False,
|
||
lines=2,
|
||
)
|
||
with gr.Row():
|
||
button1c_open = gr.Button(
|
||
value=process_info(process_name_1c, "open"), variant="primary", visible=True
|
||
)
|
||
button1c_close = gr.Button(
|
||
value=process_info(process_name_1c, "close"), variant="primary", visible=False
|
||
)
|
||
with gr.Row():
|
||
info1c = gr.Textbox(label=process_info(process_name_1c, "info"))
|
||
with gr.Row():
|
||
if_auto_sovits = gr.Checkbox(
|
||
label=i18n("自动开启SoVITS训练"),
|
||
value=False,
|
||
interactive=True,
|
||
show_label=True,
|
||
)
|
||
if_auto_gpt = gr.Checkbox(
|
||
label=i18n("自动开启GPT训练"),
|
||
value=False,
|
||
interactive=True,
|
||
show_label=True,
|
||
)
|
||
with gr.Row():
|
||
info1abc = gr.Textbox(label=process_info(process_name_1abc, "info"))
|
||
button1abc_open = gr.Button(
|
||
value="4."+process_info(process_name_1abc, "open"), variant="primary", visible=True
|
||
)
|
||
button1abc_close = gr.Button(
|
||
value="4."+process_info(process_name_1abc, "close"), variant="primary", visible=False
|
||
)
|
||
|
||
pretrained_s2G.change(sync, [pretrained_s2G], [pretrained_s2G_])
|
||
|
||
def conditional_open_asr(auto_asr, *args):
|
||
if auto_asr:
|
||
yield from open_asr(*args)
|
||
else:
|
||
yield [
|
||
"跳过语音识别",
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
gr.update(),
|
||
gr.update(),
|
||
gr.update()
|
||
]
|
||
def conditional_close_asr(auto_asr, *args):
|
||
if auto_asr:
|
||
yield from close_asr(*args)
|
||
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],
|
||
).then(
|
||
conditional_open_asr,
|
||
[if_auto_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_slicer_button.click(
|
||
close_slice,
|
||
[],
|
||
[slicer_info, open_slicer_button, close_slicer_button]
|
||
).then(
|
||
conditional_close_asr,
|
||
[if_auto_asr],
|
||
[asr_info, open_asr_button, close_asr_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])
|
||
|
||
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])
|
||
|
||
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])
|
||
def conditional_open1Ba(auto_sovits, *args):
|
||
if auto_sovits:
|
||
yield from open1Ba(*args)
|
||
else:
|
||
yield [
|
||
"跳过SoVITS训练",
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
gr.update(),
|
||
gr.update()
|
||
]
|
||
def conditional_close1Ba(auto_sovits, *args):
|
||
if auto_sovits:
|
||
yield from close1Ba(*args)
|
||
def conditional_open1Bb(auto_gpt, *args):
|
||
if auto_gpt:
|
||
yield from open1Bb(*args)
|
||
else:
|
||
yield [
|
||
"跳过GPT训练",
|
||
{"__type__": "update", "visible": True},
|
||
{"__type__": "update", "visible": False},
|
||
gr.update(),
|
||
gr.update()
|
||
]
|
||
def conditional_close1Bb(auto_gpt, *args):
|
||
if auto_gpt:
|
||
yield from close1Bb(*args)
|
||
|
||
# SoVITS 训练
|
||
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=3,
|
||
interactive=True,
|
||
)
|
||
total_epoch = gr.Slider(
|
||
minimum=1,
|
||
maximum=100,
|
||
step=1,
|
||
label=i18n("总训练轮数total_epoch，不建议太高"),
|
||
value=20,
|
||
interactive=True,
|
||
)
|
||
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=5,
|
||
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 not in v3v4set else False,
|
||
) # v3v4 not need
|
||
lora_rank = gr.Radio(
|
||
label=i18n("LoRA秩"),
|
||
value="32",
|
||
choices=["16", "32", "64", "128"],
|
||
visible=True if version in v3v4set else False,
|
||
) # v1v2 not need
|
||
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.Column(visible=False):
|
||
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 in v3v4set else False,
|
||
show_label=True,
|
||
visible=False,
|
||
) # 只有V3s2可以用
|
||
with gr.Row():
|
||
info1Ba = gr.Textbox(label=process_info(process_name_sovits, "info"))
|
||
button1Ba_open = gr.Button(
|
||
value="5."+process_info(process_name_sovits, "open"), variant="primary", visible=True
|
||
)
|
||
button1Ba_close = gr.Button(
|
||
value="5."+process_info(process_name_sovits, "close"), variant="primary", visible=False
|
||
)
|
||
|
||
# GPT 训练
|
||
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=3,
|
||
interactive=True,
|
||
)
|
||
total_epoch1Bb = gr.Slider(
|
||
minimum=2,
|
||
maximum=100,
|
||
step=1,
|
||
label=i18n("总训练轮数total_epoch"),
|
||
value=15,
|
||
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,
|
||
)
|
||
with gr.Row():
|
||
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,
|
||
)
|
||
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.Column(visible=False):
|
||
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():
|
||
info1Bb = gr.Textbox(label=process_info(process_name_gpt, "info"))
|
||
button1Bb_open = gr.Button(
|
||
value="6."+process_info(process_name_gpt, "open"), variant="primary", visible=True
|
||
)
|
||
button1Bb_close = gr.Button(
|
||
value="6."+process_info(process_name_gpt, "close"), variant="primary", visible=False
|
||
)
|
||
|
||
button1Ba_close.click(close1Ba, [], [info1Ba, button1Ba_open, button1Ba_close]).then(
|
||
conditional_close1Bb,
|
||
[if_auto_gpt], [info1Bb, button1Bb_open, button1Bb_close]
|
||
)
|
||
button1Bb_close.click(close1Bb, [], [info1Bb, button1Bb_open, button1Bb_close])
|
||
|
||
with gr.TabItem(i18n("推理")):
|
||
with gr.Row():
|
||
GPT_dropdown = gr.Dropdown(
|
||
label=i18n("GPT模型列表"),
|
||
choices=sorted(GPT_names, key=custom_sort_key),
|
||
value="GPT_SoVITS/pretrained_models/s1v3.ckpt",
|
||
interactive=True,
|
||
)
|
||
SoVITS_dropdown = gr.Dropdown(
|
||
label=i18n("SoVITS模型列表"),
|
||
choices=sorted(SoVITS_names, key=custom_sort_key),
|
||
value="GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
|
||
interactive=True,
|
||
)
|
||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
|
||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||
with gr.Row():
|
||
gpu_number_1C = gr.Textbox(label=i18n("GPU卡号，只能填1个整数"), value=gpus, interactive=True)
|
||
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():
|
||
tts_info = gr.Textbox(label=process_info(process_name_tts, "info"))
|
||
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.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],
|
||
)
|
||
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,SoVITS_dropdown,GPT_dropdown],
|
||
).then(
|
||
conditional_open1Bb,
|
||
[
|
||
if_auto_gpt,
|
||
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,SoVITS_dropdown,GPT_dropdown],
|
||
)
|
||
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,SoVITS_dropdown,GPT_dropdown],
|
||
)
|
||
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,
|
||
],
|
||
)
|
||
|
||
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],
|
||
).then(
|
||
conditional_open1Ba,
|
||
[
|
||
if_auto_sovits,
|
||
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,SoVITS_dropdown,GPT_dropdown],
|
||
).then(
|
||
conditional_open1Bb,
|
||
[
|
||
if_auto_gpt,
|
||
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,SoVITS_dropdown,GPT_dropdown],
|
||
)
|
||
button1abc_close.click(close1abc, [], [info1abc, button1abc_open, button1abc_close]).then(
|
||
conditional_close1Ba,
|
||
[if_auto_sovits], [info1Ba, button1Ba_open, button1Ba_close]
|
||
).then(
|
||
conditional_close1Bb,
|
||
[if_auto_gpt], [info1Bb, button1Bb_open, button1Bb_close]
|
||
)
|
||
|
||
with gr.TabItem(i18n("其他工具")):
|
||
# UVR5人声伴奏分离&去混响去延迟工具
|
||
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.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
|
||
close_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5])
|
||
|
||
# 新增：清空输出目录功能
|
||
with gr.Row():
|
||
clear_output_info = gr.Textbox(label=i18n("清空输出目录状态"), value="", interactive=False)
|
||
clear_output_button = gr.Button(value=i18n("清空输出目录"), variant="stop")
|
||
def clear_output_directory():
|
||
output_dir = "./output"
|
||
if os.path.exists(output_dir):
|
||
try:
|
||
shutil.rmtree(output_dir) # 删除整个目录
|
||
return i18n("输出目录已成功清空")
|
||
except Exception as e:
|
||
return f"{i18n('清空输出目录时出错')}: {str(e)}"
|
||
else:
|
||
return i18n("输出目录不存在，无需清空")
|
||
clear_output_button.click(fn=clear_output_directory, inputs=[], outputs=[clear_output_info])
|
||
|
||
app.queue().launch( # concurrency_count=511, max_size=1022
|
||
server_name="0.0.0.0",
|
||
inbrowser=True,
|
||
share=is_share,
|
||
server_port=webui_port_main,
|
||
# quiet=True,
|
||
)
|