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webui.py
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import os
import sys
if len(sys.argv) == 1:
sys.argv.append("v2")
version = "v1" if sys.argv[1] == "v1" else "v2"
os.environ["version"] = version
now_dir = os.getcwd()
sys.path.insert(0, now_dir)
import warnings
warnings.filterwarnings("ignore")
import json
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)
# if version == "v3" and minmem < 14:
# # API读取不到共享显存,直接填充确认
# try:
# torch.zeros((1024,1024,1024,14),dtype=torch.int8,device="cuda")
# torch.cuda.empty_cache()
# minmem = 14
# except RuntimeError as _:
# # 强制梯度检查只需要12G显存
# if minmem >= 12 :
# if_force_ckpt = True
# minmem = 14
# else:
# try:
# torch.zeros((1024,1024,1024,12),dtype=torch.int8,device="cuda")
# torch.cuda.empty_cache()
# if_force_ckpt = True
# minmem = 14
# except RuntimeError as _:
# print("显存不足以开启V3训练")
default_batch_size = minmem // 2 if version 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:
_ += f"\n {i}"
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, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_parts):
global ps_slice
inp = my_utils.clean_path(inp)
opt_root = my_utils.clean_path(opt_root)
check_for_existance([inp])
if os.path.exists(inp) == False:
yield (
i18n("输入路径不存在"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
{"__type__": "update"},
{"__type__": "update"},
{"__type__": "update"},
)
return
if os.path.isfile(inp):
n_parts = 1
elif os.path.isdir(inp):
pass
else:
yield (
i18n("输入路径存在但不可用"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
{"__type__": "update"},
{"__type__": "update"},
{"__type__": "update"},
)
return
if ps_slice == []:
for i_part in range(n_parts):
cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s' % (
python_exec,
inp,
opt_root,
threshold,
min_length,
min_interval,
hop_size,
max_sil_kept,
_max,
alpha,
i_part,
n_parts,
)
print(cmd)
p = Popen(cmd, shell=True)
ps_slice.append(p)
yield (
process_info(process_name_slice, "opened"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
{"__type__": "update"},
{"__type__": "update"},
{"__type__": "update"},
)
for p in ps_slice:
p.wait()
ps_slice = []
yield (
process_info(process_name_slice, "finish"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
{"__type__": "update", "value": opt_root},
{"__type__": "update", "value": opt_root},
{"__type__": "update", "value": opt_root},
)
else:
yield (
process_info(process_name_slice, "occupy"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
{"__type__": "update"},
{"__type__": "update"},
{"__type__": "update"},
)
def close_slice():
global ps_slice
if ps_slice != []:
for p_slice in ps_slice:
try:
kill_process(p_slice.pid, process_name_slice)
except:
traceback.print_exc()
ps_slice = []
return (
process_info(process_name_slice, "closed"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
)
ps1a = []
process_name_1a = i18n("文本分词与特征提取")
def open1a(inp_text, inp_wav_dir, exp_name, gpu_numbers, bert_pretrained_dir):
global ps1a
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
if ps1a == []:
opt_dir = "%s/%s" % (exp_root, exp_name)
config = {
"inp_text": inp_text,
"inp_wav_dir": inp_wav_dir,
"exp_name": exp_name,
"opt_dir": opt_dir,
"bert_pretrained_dir": bert_pretrained_dir,
}
gpu_names = gpu_numbers.split("-")
all_parts = len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
"is_half": str(is_half),
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py' % python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1a.append(p)
yield (
process_info(process_name_1a, "running"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
)
for p in ps1a:
p.wait()
opt = []
for i_part in range(all_parts):
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
with open(txt_path, "r", encoding="utf8") as f:
opt += f.read().strip("\n").split("\n")
os.remove(txt_path)
path_text = "%s/2-name2text.txt" % opt_dir
with open(path_text, "w", encoding="utf8") as f:
f.write("\n".join(opt) + "\n")
ps1a = []
if len("".join(opt)) > 0:
yield (
process_info(process_name_1a, "finish"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
)
else:
yield (
process_info(process_name_1a, "failed"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
)
else:
yield (
process_info(process_name_1a, "occupy"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
)
def close1a():
global ps1a
if ps1a != []:
for p1a in ps1a:
try:
kill_process(p1a.pid, process_name_1a)
except:
traceback.print_exc()
ps1a = []
return (
process_info(process_name_1a, "closed"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
)
ps1b = []
process_name_1b = i18n("语音自监督特征提取")
def open1b(inp_text, inp_wav_dir, exp_name, gpu_numbers, ssl_pretrained_dir):
global ps1b
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True):
check_details([inp_text, inp_wav_dir], is_dataset_processing=True)
if ps1b == []:
config = {
"inp_text": inp_text,
"inp_wav_dir": inp_wav_dir,
"exp_name": exp_name,
"opt_dir": "%s/%s" % (exp_root, exp_name),
"cnhubert_base_dir": ssl_pretrained_dir,
"is_half": str(is_half),
}
gpu_names = gpu_numbers.split("-")
all_parts = len(gpu_names)
for i_part in range(all_parts):
config.update(
{
"i_part": str(i_part),
"all_parts": str(all_parts),
"_CUDA_VISIBLE_DEVICES": fix_gpu_number(gpu_names[i_part]),
}
)
os.environ.update(config)
cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py' % python_exec
print(cmd)
p = Popen(cmd, shell=True)
ps1b.append(p)
yield (
process_info(process_name_1b, "running"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
)
for p in ps1b:
p.wait()
ps1b = []
yield (
process_info(process_name_1b, "finish"),
{"__type__": "update", "visible": True},
{"__type__": "update", "visible": False},
)
else:
yield (
process_info(process_name_1b, "occupy"),
{"__type__": "update", "visible": False},
{"__type__": "update", "visible": True},
)