Merge branch 'develop' into scheduled_message_send

clean.py

@@ -0,0 +1,6 @@
+import shutil
+
+shutil.rmtree('build', ignore_errors=True)
+shutil.rmtree('dist', ignore_errors=True)
+shutil.rmtree('src-tauri\\bin', ignore_errors=True)
+shutil.rmtree('src-tauri\\target', ignore_errors=True)

package.json

@@ -12,11 +12,12 @@
     "vite-preview": "vite preview",
     "tauri": "tauri",
     "tauri-dev": "tauri dev",
+    "clean": "python clean.py",
     "dev": "npm run build-python && npm run dev-ui",
     "dev-cuda": "npm run build-python-cuda && npm run dev-ui",
     "dev-ui": "npm-run-all --parallel vite tauri-dev",
-    "build": "npm run build-python && npm run vite-build && npm run tauri build",
+    "build": "npm run clean && npm run build-python && npm run vite-build && npm run tauri build",
-    "build-cuda": "npm run build-python-cuda && npm run vite-build && npm run tauri build",
+    "build-cuda": "npm run clean && npm run build-python-cuda && npm run vite-build && npm run tauri build",
     "release": "npm run build && python zip.py --zip_name VRCT.zip",
     "release-cuda": "npm run build-cuda && python zip.py --zip_name VRCT_cuda.zip",
     "release-all": "npm run release && npm run release-cuda"

src-python/config.py

@@ -933,7 +933,7 @@ class Config:
 
     def init_config(self):
         # Read Only
-        self._VERSION = "3.0.1"
+        self._VERSION = "3.0.2"
         if getattr(sys, 'frozen', False):
             self._PATH_LOCAL = os_path.dirname(sys.executable)
         else:

src-python/controller.py

@@ -1476,6 +1476,7 @@ class Controller:
                 )
         else:
             model.downloadCTranslate2ModelWeight(weight_type, download_ctranslate2.progressBar, download_ctranslate2.downloaded)
+        model.downloadCTranslate2ModelTokenizer(weight_type)
         return {"status":200, "result":True}
 
     def downloadWhisperWeight(self, data:str, asynchronous:bool=True, *args, **kwargs) -> dict:
@@ -1637,10 +1638,22 @@ class Controller:
         config.SELECTABLE_WHISPER_WEIGHT_TYPE_DICT = weight_type_dict
 
     def updateTranscriptionEngine(self):
-        weight_type_dict = config.SELECTABLE_WHISPER_WEIGHT_TYPE_DICT
         weight_type = config.WHISPER_WEIGHT_TYPE
-        if config.SELECTED_TRANSCRIPTION_ENGINE == "Whisper" and weight_type_dict[weight_type] is False:
+        weight_type_dict = config.SELECTABLE_WHISPER_WEIGHT_TYPE_DICT
-            config.SELECTED_TRANSCRIPTION_ENGINE = "Google"
+        weight_available = bool(weight_type_dict.get(weight_type))
+        current_engine = config.SELECTED_TRANSCRIPTION_ENGINE
+        selected_engines = [key for key, value in config.SELECTABLE_TRANSCRIPTION_ENGINE_STATUS.items() if value is True]
+
+        # 選択可能なエンジンがなければ、Whisper に変更
+        if current_engine in {"Whisper", "Google"}:
+            if current_engine not in selected_engines:
+                if weight_available:
+                    alternate = "Google" if current_engine == "Whisper" else "Whisper"
+                    config.SELECTED_TRANSCRIPTION_ENGINE = alternate if alternate in selected_engines else None
+                else:
+                    config.SELECTED_TRANSCRIPTION_ENGINE = "Whisper"
+        else:
+            config.SELECTED_TRANSCRIPTION_ENGINE = "Whisper"
 
     def startCheckMicEnergy(self) -> None:
         while self.device_access_status is False:
@@ -1728,20 +1741,6 @@ class Controller:
             self.disconnectedNetwork()
         printLog(f"Connected Network: {connected_network}")
 
-        printLog("Init Translation Engine Status")
-        for engine in config.SELECTABLE_TRANSLATION_ENGINE_LIST:
-            match engine:
-                case "DeepL_API":
-                    printLog("Start check DeepL API Key")
-                    config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = False
-                    if config.AUTH_KEYS[engine] is not None:
-                        if model.authenticationTranslatorDeepLAuthKey(auth_key=config.AUTH_KEYS[engine]) is True:
-                            config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = True
-                        else:
-                            # error update Auth key
-                            auth_keys = config.AUTH_KEYS
-                            auth_keys[engine] = None
-                            config.AUTH_KEYS = auth_keys
         self.initializationProgress(1)
 
         if connected_network is True:
@@ -1774,6 +1773,7 @@ class Controller:
         else:
             self.enableAiModels()
 
+        printLog("Init Translation Engine Status")
         for engine in config.SELECTABLE_TRANSLATION_ENGINE_LIST:
             match engine:
                 case "CTranslate2":
@@ -1781,6 +1781,17 @@ class Controller:
                         config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = True
                     else:
                         config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = False
+                case "DeepL_API":
+                    printLog("Start check DeepL API Key")
+                    config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = False
+                    if config.AUTH_KEYS[engine] is not None:
+                        if model.authenticationTranslatorDeepLAuthKey(auth_key=config.AUTH_KEYS[engine]) is True:
+                            config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = True
+                        else:
+                            # error update Auth key
+                            auth_keys = config.AUTH_KEYS
+                            auth_keys[engine] = None
+                            config.AUTH_KEYS = auth_keys
                 case _:
                     if connected_network is True:
                         config.SELECTABLE_TRANSLATION_ENGINE_STATUS[engine] = True

src-python/model.py

@@ -24,7 +24,7 @@ from models.transcription.transcription_recorder import SelectedMicEnergyRecorde
 from models.transcription.transcription_transcriber import AudioTranscriber
 from models.translation.translation_languages import translation_lang
 from models.transcription.transcription_languages import transcription_lang
-from models.translation.translation_utils import checkCTranslate2Weight, downloadCTranslate2Weight
+from models.translation.translation_utils import checkCTranslate2Weight, downloadCTranslate2Weight, downloadCTranslate2Tokenizer
 from models.transcription.transcription_whisper import checkWhisperWeight, downloadWhisperWeight
 from models.overlay.overlay import Overlay
 from models.overlay.overlay_image import OverlayImage
@@ -113,6 +113,9 @@ class Model:
     def downloadCTranslate2ModelWeight(self, weight_type, callback=None, end_callback=None):
         return downloadCTranslate2Weight(config.PATH_LOCAL, weight_type, callback, end_callback)
 
+    def downloadCTranslate2ModelTokenizer(self, weight_type):
+        return downloadCTranslate2Tokenizer(config.PATH_LOCAL, weight_type)
+
     def isLoadedCTranslate2Model(self):
         return self.translator.isLoadedCTranslate2Model()
 

src-python/models/translation/translation_utils.py

@@ -5,6 +5,7 @@ from os import makedirs as os_makedirs
 from requests import get as requests_get
 from typing import Callable
 import hashlib
+import transformers
 from utils import errorLogging
 
 ctranslate2_weights = {
@@ -86,4 +87,17 @@ def downloadCTranslate2Weight(root, weight_type="small", callback=None, end_call
             errorLogging()
 
     if isinstance(end_callback, Callable):
-        end_callback()
+        end_callback()
+
+def downloadCTranslate2Tokenizer(path, weight_type="small"):
+    directory_name = ctranslate2_weights[weight_type]["directory_name"]
+    tokenizer = ctranslate2_weights[weight_type]["tokenizer"]
+    tokenizer_path = os_path.join(path, "weights", "ctranslate2", directory_name, "tokenizer")
+
+    try:
+        os_makedirs(tokenizer_path, exist_ok=True)
+        transformers.AutoTokenizer.from_pretrained(tokenizer, cache_dir=tokenizer_path)
+    except Exception:
+        errorLogging()
+        tokenizer_path = os_path.join("./weights", "ctranslate2", directory_name, "tokenizer")
+        transformers.AutoTokenizer.from_pretrained(tokenizer, cache_dir=tokenizer_path)