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fixed some bugs, and add some checking

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This commit is contained in:
XXXXRT666 2024-08-08 02:06:08 +08:00
parent a71c1a8481
commit f5c01be48f
5 changed files with 99 additions and 41 deletions
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3
GPT_SoVITS/prepare_datasets/1-get-text.py
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@ -7,7 +7,8 @@ inp_wav_dir = os.environ.get("inp_wav_dir")
exp_name = os.environ.get("exp_name")
i_part = os.environ.get("i_part")
all_parts = os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
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

3
GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py
View File

@ -6,7 +6,8 @@ inp_wav_dir= os.environ.get("inp_wav_dir")
exp_name= os.environ.get("exp_name")
i_part= os.environ.get("i_part")
all_parts= os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
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")

3
GPT_SoVITS/prepare_datasets/3-get-semantic.py
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@ -4,7 +4,8 @@ inp_text = os.environ.get("inp_text")
exp_name = os.environ.get("exp_name")
i_part = os.environ.get("i_part")
all_parts = os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
opt_dir = os.environ.get("opt_dir")
pretrained_s2G = os.environ.get("pretrained_s2G")
s2config_path = os.environ.get("s2config_path")

79
tools/my_utils.py
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@ -1,7 +1,10 @@
import platform,os,traceback
import ffmpeg
import numpy as np
import gradio as gr
from i18n.i18n import I18nAuto
import pandas as pd
i18n = I18nAuto(language=os.environ.get('language','Auto'))
def load_audio(file, sr):
try:
@ -20,7 +23,7 @@ def load_audio(file, sr):
)
except Exception as e:
traceback.print_exc()
raise RuntimeError(f"Failed to load audio: {e}")
raise RuntimeError(i18n("Failed to load audio:")+e)
return np.frombuffer(out, np.float32).flatten()
@ -30,3 +33,75 @@ def clean_path(path_str:str):
return clean_path(path_str[0:-1])
path_str = path_str.replace('/', os.sep).replace('\\', os.sep)
return path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
def check_for_existance(file_list:list=None,is_train=False,is_dataset_processing=False):
files_status=[]
if is_train == True and file_list:
file_list.append(os.path.join(file_list[0],'2-name2text.txt'))
file_list.append(os.path.join(file_list[0],'3-bert'))
file_list.append(os.path.join(file_list[0],'4-cnhubert'))
file_list.append(os.path.join(file_list[0],'5-wav32k'))
file_list.append(os.path.join(file_list[0],'6-name2semantic.tsv'))
for file in file_list:
if os.path.exists(file):files_status.append(True)
else:files_status.append(False)
if sum(files_status)!=0:
if is_train:
for file,status in zip(file_list,files_status):
if status:pass
else:gr.Warning(file)
gr.Warning(i18n('以下文件或文件夹不存在:'))
elif is_dataset_processing:
if not files_status[0]:
gr.Warning(file_list[0])
if not files_status[1] and file_list[1]:
gr.Warning(file_list[1])
gr.Warning(i18n('以下文件或文件夹不存在:'))
else:
if file_list[0]:
gr.Warning(file_list[0])
gr.Warning(i18n('以下文件或文件夹不存在:'))
else:
gr.Warning(i18n('路径不能为空'))
def check_details(path_list=None,is_train=False,is_dataset_processing=False):
if is_dataset_processing:
list_path, audio_path = path_list
if (not list_path.endswith('.list')):
gr.Warning(i18n('请填入正确的list路径'))
return
if audio_path:
if not os.path.isdir(audio_path):
gr.Warning(i18n('请填入正确的音频文件夹路径'))
return
with open(list_path,"r",encoding="utf8")as f:
line=f.readline().strip("\n").split("\n")
wav_name, spk_name, language, text = line.split("|")
wav_name=clean_path(wav_name)
if (audio_path != "" and audio_path != None):
wav_name = os.path.basename(wav_name)
wav_path = "%s/%s"%(audio_path, wav_name)
else:
wav_path=wav_name
if os.path.exists(wav_path):
...
else:
gr.Warning(i18n('路径错误'))
if is_train:
path_list.append(os.path.join(path_list[0],'2-name2text.txt'))
path_list.append(os.path.join(path_list[0],'4-cnhubert'))
path_list.append(os.path.join(path_list[0],'5-wav32k'))
path_list.append(os.path.join(path_list[0],'6-name2semantic.tsv'))
phone_path, hubert_path, wav_path, semantic_path = path_list[1:]
with open(phone_path) as f:
if f.read(1):...
else:gr.Warning(i18n('缺少音素数据集'))
if os.listdir(hubert_path):...
else:gr.Warning(i18n('缺少Hubert数据集'))
if os.listdir(wav_path):...
else:gr.Warning(i18n('缺少音频数据集'))
df = pd.read_csv(semantic_path)
if len(pd) > 1:...
else:gr.Warning(i18n('缺少语义数据集'))

52
webui.py
View File

@ -56,7 +56,7 @@ 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
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:
@ -248,7 +248,7 @@ def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_
if(p_asr==None):
asr_inp_dir=my_utils.clean_path(asr_inp_dir)
asr_opt_dir=my_utils.clean_path(asr_opt_dir)
check_for_exists([asr_inp_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}"'
@ -279,7 +279,7 @@ def open_denoise(denoise_inp_dir, denoise_opt_dir):
if(p_denoise==None):
denoise_inp_dir=my_utils.clean_path(denoise_inp_dir)
denoise_opt_dir=my_utils.clean_path(denoise_opt_dir)
check_for_exists([denoise_inp_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 "语音降噪任务开启：%s"%cmd, {"__type__":"update","visible":False}, {"__type__":"update","visible":True}, {"__type__":"update"}, {"__type__":"update"}
@ -308,7 +308,8 @@ def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_s
data=json.loads(data)
s2_dir="%s/%s"%(exp_root,exp_name)
os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
check_for_exists([s2_dir],is_train=True)
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)
@ -355,7 +356,8 @@ def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_
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)
check_for_exists([s1_dir],is_train=True)
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)
@ -400,7 +402,7 @@ def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_k
global ps_slice
inp = my_utils.clean_path(inp)
opt_root = my_utils.clean_path(opt_root)
check_for_exists([inp])
check_for_existance([inp])
if(os.path.exists(inp)==False):
yield "输入路径不存在", {"__type__":"update","visible":True}, {"__type__":"update","visible":False}, {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
return
@ -439,7 +441,8 @@ 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)
check_for_exists([inp_text,inp_wav_dir], is_dataset_processing=True)
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={
@ -501,7 +504,8 @@ 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)
check_for_exists([inp_text,inp_wav_dir], is_dataset_processing=True)
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,
@ -549,7 +553,8 @@ ps1c=[]
def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
global ps1c
inp_text = my_utils.clean_path(inp_text)
check_for_exists([inp_text,''], is_dataset_processing=True)
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={
@ -608,7 +613,8 @@ def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numb
global ps1abc
inp_text = my_utils.clean_path(inp_text)
inp_wav_dir = my_utils.clean_path(inp_wav_dir)
check_for_exists([inp_text,inp_wav_dir])
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:
@ -745,32 +751,6 @@ def switch_version(version_):
gr.Warning(i18n(f'未下载{version.upper()}模型'))
return {'__type__':'update', 'value':pretrained_sovits_name[-int(version[-1])+2]}, {'__type__':'update', 'value':pretrained_sovits_name[-int(version[-1])+2].replace("s2G","s2D")}, {'__type__':'update', 'value':pretrained_gpt_name[-int(version[-1])+2]}, {'__type__':'update', 'value':pretrained_gpt_name[-int(version[-1])+2]}, {'__type__':'update', 'value':pretrained_sovits_name[-int(version[-1])+2]}
def check_for_exists(file_list=None,is_train=False,is_dataset_processing=False):
missing_files=[]
if is_train == True and file_list:
file_list.append(os.path.join(file_list[0],'2-name2text.txt'))
file_list.append(os.path.join(file_list[0],'3-bert'))
file_list.append(os.path.join(file_list[0],'4-cnhubert'))
file_list.append(os.path.join(file_list[0],'5-wav32k'))
file_list.append(os.path.join(file_list[0],'6-name2semantic.tsv'))
for file in file_list:
if os.path.exists(file):pass
else:missing_files.append(file)
if missing_files:
if is_train:
for missing_file in missing_files:
if missing_file != '':
gr.Warning(missing_file)
gr.Warning(i18n('以下文件或文件夹不存在:'))
else:
for missing_file in missing_files:
if missing_file != '':
gr.Warning(missing_file)
if file_list[-1]==[''] and is_dataset_processing:
pass
else:
gr.Warning(i18n('以下文件或文件夹不存在:'))
if os.path.exists('GPT_SoVITS/text/G2PWModel'):...
else:
cmd = '"%s" GPT_SoVITS/download.py'%python_exec