fixed some bugs, and add some checking

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -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

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

@@ -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")

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -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")

tools/my_utils.py

@@ -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'))

webui.py

@@ -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