Redownload XTTS with the local and remote config do not match

TTS/utils/manage.py

@@ -6,6 +6,7 @@ from pathlib import Path
 from shutil import copyfile, rmtree
 from typing import Dict, List, Tuple
 
+import fsspec
 import requests
 from tqdm import tqdm
 
@@ -320,26 +321,10 @@ class ModelManager(object):
             return False
         return True
 
-    def download_model(self, model_name):
+    def check_if_files_size(self, model_name):
-        """Download model files given the full model name.
+        pass
-        Model name is in the format
-            'type/language/dataset/model'
-            e.g. 'tts_model/en/ljspeech/tacotron'
 
-        Every model must have the following files:
+    def create_dir_and_download_model(self, model_name, model_item, output_path):
-            - *.pth : pytorch model checkpoint file.
-            - config.json : model config file.
-            - scale_stats.npy (if exist): scale values for preprocessing.
-
-        Args:
-            model_name (str): model name as explained above.
-        """
-        model_item, model_full_name, model = self._set_model_item(model_name)
-        # set the model specific output path
-        output_path = os.path.join(self.output_prefix, model_full_name)
-        if os.path.exists(output_path):
-            print(f" > {model_name} is already downloaded.")
-        else:
         os.makedirs(output_path, exist_ok=True)
         # handle TOS
         if not self.tos_agreed(model_item, output_path):
@@ -360,6 +345,47 @@ class ModelManager(object):
             rmtree(output_path)
             raise e
         self.print_model_license(model_item=model_item)
+
+    def download_model(self, model_name):
+        """Download model files given the full model name.
+        Model name is in the format
+            'type/language/dataset/model'
+            e.g. 'tts_model/en/ljspeech/tacotron'
+
+        Every model must have the following files:
+            - *.pth : pytorch model checkpoint file.
+            - config.json : model config file.
+            - scale_stats.npy (if exist): scale values for preprocessing.
+
+        Args:
+            model_name (str): model name as explained above.
+        """
+        model_item, model_full_name, model = self._set_model_item(model_name)
+        # set the model specific output path
+        output_path = os.path.join(self.output_prefix, model_full_name)
+        if os.path.exists(output_path):
+            # if the configs are different, redownload it
+            # ToDo: we need a better way to handle it
+            if "xtts_v1" in model_name:
+                with fsspec.open(self._find_files(output_path)[1], "r", encoding="utf-8") as f:
+                    config_local = json.load(f)
+                remote_url = None
+                for url in model_item["hf_url"]:
+                    if "config.json" in url:
+                        remote_url = url
+                        break
+
+                with fsspec.open(remote_url, "r", encoding="utf-8") as f:
+                    config_remote = json.load(f)
+
+                if not config_local == config_remote:
+                    print(f" > {model_name} is already downloaded however it has been changed. Redownloading it...")
+                    self.create_dir_and_download_model(model_name, model_item, output_path)
+            else:
+                print(f" > {model_name} is already downloaded.")
+        else:
+            self.create_dir_and_download_model(model_name, model_item, output_path)
+
         # find downloaded files
         output_model_path = output_path
         output_config_path = None