Updated to deal with Gradio 4.xx versions

finetune.py

@@ -778,15 +778,15 @@ def get_available_voices(minimum_size_kb=1200):
 
 def find_best_models(directory):
     """Find files named 'best_model.pth' in the given directory."""
-    return [file for file in Path(directory).rglob("best_model.pth")]
+    return [str(file) for file in Path(directory).rglob("best_model.pth")]
 
 def find_models(directory, extension):
     """Find files with a specific extension in the given directory."""
-    return [file for file in Path(directory).rglob(f"*.{extension}")]
+    return [str(file) for file in Path(directory).rglob(f"*.{extension}")]
 
 def find_jsons(directory, filename):
     """Find files with a specific filename in the given directory."""
-    return list(Path(directory).rglob(filename))
+    return [str(file) for file in Path(directory).rglob(filename)]
 
 # Your main directory
 main_directory = Path(this_dir) / "finetune" / "tmp-trn"
@@ -809,22 +809,21 @@ def find_latest_best_model(folder_path):
 
 def compact_model(xtts_checkpoint_copy):
     this_dir = Path(__file__).parent.resolve()
-    best_model_path_str = xtts_checkpoint_copy
+    print("THIS DIR:", this_dir)
+    best_model_path_str = str(xtts_checkpoint_copy)  # Convert to string
+    print("best_model_path_str", best_model_path_str)
 
     # Check if the best model file exists
-    if best_model_path_str is None:
+    if not best_model_path_str:
         print("[FINETUNE] No trained model was found.")
         return "No trained model was found."
 
     print(f"[FINETUNE] Best model path: {best_model_path_str}")
 
-    # Convert model_path_str to Path
-    best_model_path = Path(best_model_path_str)
-
     # Attempt to load the model
     try:
-        checkpoint = torch.load(best_model_path, map_location=torch.device("cpu"))
-        print(f"[FINETUNE] Checkpoint loaded: {best_model_path}")
+        checkpoint = torch.load(best_model_path_str, map_location=torch.device("cpu"))
+        print(f"[FINETUNE] Checkpoint loaded: {best_model_path_str}")
     except Exception as e:
         print("[FINETUNE] Error loading checkpoint:", e)
         raise
@@ -842,15 +841,15 @@ def compact_model(xtts_checkpoint_copy):
             del checkpoint["model"][key]
 
     # Save the modified checkpoint in the target directory
-    torch.save(checkpoint, target_dir / "model.pth")
+    torch.save(checkpoint, str(target_dir / "model.pth"))  # Convert to string
 
     # Specify the files you want to copy
-    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
+    files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth"]
 
     for file_name in files_to_copy:
         src_path = this_dir / base_path / base_model_path / file_name
         dest_path = target_dir / file_name
-        shutil.copy(str(src_path), str(dest_path))
+        shutil.copy(str(src_path), str(dest_path))  # Convert to string
 
     source_wavs_dir = this_dir / "finetune" / "tmp-trn" / "wavs"
     target_wavs_dir = target_dir / "wavs"
@@ -861,7 +860,7 @@ def compact_model(xtts_checkpoint_copy):
         # Check if it's a file and larger than 1000 KB
         if file_path.is_file() and file_path.stat().st_size > 1000 * 1024:
             # Copy the file to the target directory
-            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))
+            shutil.copy(str(file_path), str(target_wavs_dir / file_path.name))  # Convert to string
 
     print("[FINETUNE] Model copied to '/models/trainedmodel/'")
     return "Model copied to '/models/trainedmodel/'"
@@ -1764,7 +1763,7 @@ def train_model(language, train_csv, eval_csv, learning_rates, num_epochs, batch
                 )
                 # Create refresh button
                 refresh_button = create_refresh_button(
-                    [xtts_checkpoint,],
+                    [xtts_checkpoint_copy,],
                     [
                         lambda: {"choices": find_best_models(main_directory), "value": ""},
                     ],