diff --git a/finetune-cli.py b/finetune-cli.py
index 2f49e969..79ce9bb0 100644
--- a/finetune-cli.py
+++ b/finetune-cli.py
@@ -28,6 +28,7 @@ def parse_args():
     parser.add_argument('--num_warmup_updates', type=int, default=5, help='Warmup steps')
     parser.add_argument('--save_per_updates', type=int, default=10, help='Save checkpoint every X steps')
     parser.add_argument('--last_per_steps', type=int, default=10, help='Save last checkpoint every X steps')
+    parser.add_argument('--finetune', type=bool, default=True, help='Use Finetune')
     
     return parser.parse_args()
 
@@ -42,17 +43,21 @@ def main():
         wandb_resume_id = None
         model_cls = DiT
         model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
-        ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+        if args.finetune:
+           ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
     elif args.exp_name == "E2TTS_Base":
         wandb_resume_id = None
         model_cls = UNetT
         model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
-        ckpt_path = str(cached_path(f"hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+        if args.finetune:
+           ckpt_path = str(cached_path(f"hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+           
+    if args.finetune:
+       path_ckpt = os.path.join("ckpts",args.dataset_name)
+       if os.path.isdir(path_ckpt)==False:
+          os.makedirs(path_ckpt,exist_ok=True)
+          shutil.copy2(ckpt_path,os.path.join(path_ckpt,os.path.basename(ckpt_path)))
     
-    path_ckpt = os.path.join("ckpts",args.dataset_name)
-    if os.path.isdir(path_ckpt)==False:
-       os.makedirs(path_ckpt,exist_ok=True)
-       shutil.copy2(ckpt_path,os.path.join(path_ckpt,os.path.basename(ckpt_path)))
     checkpoint_path=os.path.join("ckpts",args.dataset_name)
 
     # Use the dataset_name provided in the command line
diff --git a/finetune_gradio.py b/finetune_gradio.py
index 4908d787..d6db8cc4 100644
--- a/finetune_gradio.py
+++ b/finetune_gradio.py
@@ -9,24 +9,19 @@
 import librosa
 import numpy as np
 from scipy.io import wavfile
-from tqdm import tqdm
 import shutil
 import time
 
 import json
-from datasets import Dataset
 from model.utils import convert_char_to_pinyin
 import signal
 import psutil
 import platform
 import subprocess
 from datasets.arrow_writer import ArrowWriter
-from datasets import load_dataset, load_from_disk
 
 import json
 
-
-
 training_process = None    
 system = platform.system()
 python_executable = sys.executable or "python"
@@ -265,8 +260,20 @@ def start_training(dataset_name="",
     finetune=True,                    
     ):
 
+
     global training_process
 
+    path_project = os.path.join(path_data, dataset_name + "_pinyin")
+
+    if os.path.isdir(path_project)==False:
+        yield f"There is not project with name {dataset_name}",gr.update(interactive=True),gr.update(interactive=False)
+        return
+
+    file_raw = os.path.join(path_project,"raw.arrow")
+    if os.path.isfile(file_raw)==False:
+       yield f"There is no file {file_raw}",gr.update(interactive=True),gr.update(interactive=False)
+       return    
+
     # Check if a training process is already running
     if training_process is not None:
         return "Train run already!",gr.update(interactive=False),gr.update(interactive=True)
@@ -274,7 +281,7 @@ def start_training(dataset_name="",
     yield "start train",gr.update(interactive=False),gr.update(interactive=False)
 
     # Command to run the training script with the specified arguments
-    cmd = f"{python_executable} finetune-cli.py --exp_name {exp_name} " \
+    cmd = f"accelerate launch finetune-cli.py --exp_name {exp_name} " \
           f"--learning_rate {learning_rate} " \
           f"--batch_size_per_gpu {batch_size_per_gpu} " \
           f"--batch_size_type {batch_size_type} " \
@@ -346,6 +353,8 @@ def transcribe_all(name_project,audio_files,language,user=False,progress=gr.Prog
     path_project_wavs = os.path.join(path_project,"wavs")
     file_metadata = os.path.join(path_project,"metadata.csv")
 
+    if audio_files is None:return "You need to load an audio file."
+
     if os.path.isdir(path_project_wavs):
        shutil.rmtree(path_project_wavs)
 
@@ -356,16 +365,17 @@ def transcribe_all(name_project,audio_files,language,user=False,progress=gr.Prog
     
     if user:
        file_audios = [file for format in ('*.wav', '*.ogg', '*.opus', '*.mp3', '*.flac') for file in glob(os.path.join(path_dataset, format))]
+       if file_audios==[]:return "No audio file was found in the dataset."
     else:
        file_audios = audio_files
-
-    print([file_audios])   
+       
 
     alpha = 0.5
     _max = 1.0
     slicer = Slicer(24000)
 
     num = 0
+    error_num = 0
     data=""
     for file_audio in progress.tqdm(file_audios, desc="transcribe files",total=len((file_audios))):
         
@@ -381,18 +391,26 @@ def transcribe_all(name_project,audio_files,language,user=False,progress=gr.Prog
             if(tmp_max>1):chunk/=tmp_max
             chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
             wavfile.write(file_segment,24000, (chunk * 32767).astype(np.int16))
+            
+            try:
+               text=transcribe(file_segment,language)
+               text = text.lower().strip().replace('"',"")
 
-            text=transcribe(file_segment,language)
-            text = text.lower().strip().replace('"',"")
+               data+= f"{name_segment}|{text}\n"
 
-            data+= f"{name_segment}|{text}\n"
+               num+=1
+            except:
+               error_num +=1
 
-            num+=1
-    
     with open(file_metadata,"w",encoding="utf-8") as f:
         f.write(data)
-
-    return f"transcribe complete samples : {num} in path {path_project_wavs}"
+    
+    if error_num!=[]:
+       error_text=f"\nerror files : {error_num}"    
+    else:
+       error_text=""
+       
+    return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
 
 def format_seconds_to_hms(seconds):
     hours = int(seconds / 3600)
@@ -408,6 +426,8 @@ def create_metadata(name_project,progress=gr.Progress()):
     file_raw = os.path.join(path_project,"raw.arrow")
     file_duration = os.path.join(path_project,"duration.json")
     file_vocab = os.path.join(path_project,"vocab.txt")
+
+    if os.path.isfile(file_metadata)==False: return "The file was not found in " + file_metadata 
     
     with open(file_metadata,"r",encoding="utf-8") as f:
         data=f.read()
@@ -419,11 +439,18 @@ def create_metadata(name_project,progress=gr.Progress()):
     count=data.split("\n")
     lenght=0
     result=[]
+    error_files=[]
     for line in progress.tqdm(data.split("\n"),total=count):
         sp_line=line.split("|")
         if len(sp_line)!=2:continue
-        name_audio,text = sp_line[:2] 
+        name_audio,text = sp_line[:2]
+
         file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
+
+        if os.path.isfile(file_audio)==False:
+            error_files.append(file_audio)
+            continue
+
         duraction = get_audio_duration(file_audio)
         if duraction<2 and duraction>15:continue
         if len(text)<4:continue
@@ -439,6 +466,10 @@ def create_metadata(name_project,progress=gr.Progress()):
 
         lenght+=duraction
 
+    if duration_list==[]:
+        error_files_text="\n".join(error_files)
+        return f"Error: No audio files found in the specified path : \n{error_files_text}"
+    
     min_second = round(min(duration_list),2)   
     max_second = round(max(duration_list),2)
 
@@ -450,9 +481,15 @@ def create_metadata(name_project,progress=gr.Progress()):
         json.dump({"duration": duration_list}, f, ensure_ascii=False)
  
     file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"    
+    if os.path.isfile(file_vocab_finetune==False):return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
     shutil.copy2(file_vocab_finetune, file_vocab)
-
-    return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n"
+    
+    if error_files!=[]:
+       error_text="error files\n" + "\n".join(error_files)
+    else:
+       error_text=""
+        
+    return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}"
 
 def check_user(value):
     return gr.update(visible=not value),gr.update(visible=value)
@@ -466,15 +503,19 @@ def calculate_train(name_project,batch_size_type,max_samples,learning_rate,num_w
         data = json.load(file)  
     
     duration_list = data['duration']
+
     samples = len(duration_list)
 
-    gpu_properties = torch.cuda.get_device_properties(0)
-    total_memory = gpu_properties.total_memory / (1024 ** 3)  
+    if torch.cuda.is_available():
+        gpu_properties = torch.cuda.get_device_properties(0)
+        total_memory = gpu_properties.total_memory / (1024 ** 3)  
+    elif torch.backends.mps.is_available():
+        total_memory = psutil.virtual_memory().available / (1024 ** 3)
     
     if batch_size_type=="frame":
        batch = int(total_memory * 0.5)
        batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
-       batch_size_per_gpu = int(36800 / batch )
+       batch_size_per_gpu = int(38400 / batch )
     else:
        batch_size_per_gpu = int(total_memory / 8)   
        batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
@@ -509,13 +550,12 @@ def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -
         if ema_model_state_dict is not None:
             new_checkpoint = {'ema_model_state_dict': ema_model_state_dict}
             torch.save(new_checkpoint, new_checkpoint_path)
-            print(f"New checkpoint saved at: {new_checkpoint_path}")
+            return f"New checkpoint saved at: {new_checkpoint_path}"
         else:
-            print("No 'ema_model_state_dict' found in the checkpoint.")
+            return "No 'ema_model_state_dict' found in the checkpoint."
 
     except Exception as e:
-        print(f"An error occurred: {e}")
-
+        return f"An error occurred: {e}"
 
 def vocab_check(project_name):
     name_project = project_name + "_pinyin"
@@ -524,12 +564,17 @@ def vocab_check(project_name):
     file_metadata = os.path.join(path_project, "metadata.csv")
    
     file_vocab="data/Emilia_ZH_EN_pinyin/vocab.txt"
+    if os.path.isfile(file_vocab)==False:
+        return f"the file {file_vocab} not found !"
     
     with open(file_vocab,"r",encoding="utf-8") as f:
          data=f.read()
 
     vocab = data.split("\n")
 
+    if os.path.isfile(file_metadata)==False:
+        return f"the file {file_metadata} not found !"
+
     with open(file_metadata,"r",encoding="utf-8") as f:
          data=f.read()
 
@@ -548,6 +593,7 @@ def vocab_check(project_name):
             
     if miss_symbols==[]:info ="You can train using your language !"     
     else:info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)   
+
     return info
 
 
@@ -652,8 +698,9 @@ def vocab_check(project_name):
          with gr.TabItem("reduse checkpoint"):
               txt_path_checkpoint = gr.Text(label="path checkpoint :") 
               txt_path_checkpoint_small = gr.Text(label="path output :") 
+              txt_info_reduse = gr.Text(label="info",value="")
               reduse_button = gr.Button("reduse") 
-              reduse_button.click(fn=extract_and_save_ema_model,inputs=[txt_path_checkpoint,txt_path_checkpoint_small])
+              reduse_button.click(fn=extract_and_save_ema_model,inputs=[txt_path_checkpoint,txt_path_checkpoint_small],outputs=[txt_info_reduse])
 
          with gr.TabItem("vocab check experiment"):
               check_button = gr.Button("check vocab") 
@@ -680,10 +727,4 @@ def main(port, host, share, api):
     )
 
 if __name__ == "__main__":
-    name="my_speak"
-
-    #create_data_project(name)
-    #transcribe_all(name)
-    #create_metadata(name)
-
     main()