download github model releases by model manager

TTS/.models.json

@@ -14,6 +14,7 @@
                     "model_file": "1CFoPDQBnhfBFu2Gc0TBSJn8o-TuNKQn7",
                     "config_file": "1lWSscNfKet1zZSJCNirOn7v9bigUZ8C1",
                     "stats_file": "1qevpGRVHPmzfiRBNuugLMX62x1k7B5vK",
+                    "github_rls_url": null,
                     "commit": ""
                 },
                 "speedy-speech-wn":{

TTS/utils/manage.py

@@ -1,8 +1,11 @@
+import io
 import json
 import os
+import zipfile
 from pathlib import Path
 
 import gdown
+import requests
 from TTS.utils.generic_utils import get_user_data_dir
 from TTS.utils.io import load_config
 
@@ -71,6 +74,11 @@ class ModelManager(object):
             'type/language/dataset/model'
             e.g. 'tts_model/en/ljspeech/tacotron'
 
+        Every model must have the following files
+            - *.pth.tar : 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.
 
@@ -91,11 +99,17 @@ class ModelManager(object):
             print(f" > Downloading model to {output_path}")
             output_stats_path = None
             # download files to the output path
-            self._download_file(model_item['model_file'], output_model_path)
-            self._download_file(model_item['config_file'], output_config_path)
-            if model_item['stats_file'] is not None and len(model_item['stats_file']) > 1:
+            if self._check_dict_key(model_item, 'github_rls_url'):
+                # download from github release
+                # TODO: pass output_path
+                self._download_zip_file(model_item['github_rls_url'], output_path)
+            else:
+                # download from gdrive
+                self._download_gdrive_file(model_item['model_file'], output_model_path)
+                self._download_gdrive_file(model_item['config_file'], output_config_path)
+            if self._check_dict_key(model_item, 'scale_stats'):
                 output_stats_path = os.path.join(output_path, 'scale_stats.npy')
-                self._download_file(model_item['stats_file'], output_stats_path)
+                self._download_gdrive_file(model_item['stats_file'], output_stats_path)
                 # set scale stats path in config.json
                 config_path = output_config_path
                 config = load_config(config_path)
@@ -104,9 +118,25 @@ class ModelManager(object):
                     json.dump(config, jf)
         return output_model_path, output_config_path
 
-    def _download_file(self, idx, output):
-        gdown.download(f"{self.url_prefix}{idx}", output=output, quiet=False)
-
+    def _download_gdrive_file(self, gdrive_idx, output):
+        """Download files from GDrive using their file ids"""
+        gdown.download(f"{self.url_prefix}{gdrive_idx}", output=output, quiet=False)
+
+    def _download_zip_file(self, file_url, output):
+        """Download the target zip file and extract the files
+        to a folder with the same name as the zip file."""
+        r = requests.get(file_url)
+        z = zipfile.ZipFile(io.BytesIO(r.content))
+        z.extractall(output)
+
+    @staticmethod
+    def _check_dict_key(my_dict, key):
+        if key in my_dict.keys() and my_dict[key] is not None:
+            if not isinstance(key, str):
+                return True
+            if isinstance(key, str) and len(my_dict[key]) > 0:
+                return True
+        return False