👍️[Update] Model : status 348のlog を printLog関数に集約

src-python/models/overlay/overlay.py

@@ -1,4 +1,7 @@
-import os
+from os import path as os_path
+import sys
+sys.path.append(os_path.join(os_path.dirname(__file__), "..", ".."))
+from utils import printLog
 import ctypes
 import time
 from psutil import process_iter
@@ -109,8 +112,7 @@ class Overlay:
             )
 
         except Exception as e:
-            import json
-            print(json.dumps({"status":348, "log": f"error:Could not initialise OpenVR {e}"}), flush=True)
+            printLog("error:Could not initialise OpenVR", e)
 
     def updateImage(self, img):
         if self.initialized is True:
@@ -204,8 +206,7 @@ class Overlay:
                         return False
             return True
         except Exception as e:
-            import json
-            print(json.dumps({"status":348, "log": f"error:Could not check SteamVR running {e}"}), flush=True)
+            printLog("error:Could not check SteamVR running", e)
             return False
 
     def evaluateOpacityFade(self, lastUpdate, currentTime):

src-python/models/transcription/transcription_whisper.py

@@ -1,9 +1,13 @@
 from os import path as os_path, makedirs as os_makedirs
+import sys
+sys.path.append(os_path.join(os_path.dirname(__file__), "..", ".."))
+from utils import printLog
 from requests import get as requests_get
 from typing import Callable
 import huggingface_hub
 from faster_whisper import WhisperModel
 import logging
+
 logger = logging.getLogger('faster_whisper')
 logger.setLevel(logging.CRITICAL)
 
@@ -33,15 +37,14 @@ def downloadFile(url, path, func=None):
         file_size = int(res.headers.get('content-length', 0))
         total_chunk = 0
         with open(os_path.join(path), 'wb') as file:
-            for chunk in res.iter_content(chunk_size=1024*5):
+            for chunk in res.iter_content(chunk_size=1024*2000):
                 file.write(chunk)
                 if isinstance(func, Callable):
                     total_chunk += len(chunk)
                     func(total_chunk/file_size)
 
     except Exception as e:
-        import json
-        print(json.dumps({"status":348, "log": f"error:downloadFile() {e}"}), flush=True)
+        printLog("warning:downloadFile()", e)
 
 def checkWhisperWeight(root, weight_type):
     path = os_path.join(root, "weights", "whisper", weight_type)

src-python/models/translation/translation_translator.py

@@ -1,4 +1,7 @@
-import os
+from os import path as os_path
+import sys
+sys.path.append(os_path.join(os_path.dirname(__file__), "..", ".."))
+from utils import printLog
 from deepl import Translator as deepl_Translator
 from translators import translate_text as other_web_Translator
 from .translation_languages import translation_lang
@@ -32,8 +35,8 @@ class Translator():
         self.is_loaded_ctranslate2_model = False
         directory_name = ctranslate2_weights[model_type]["directory_name"]
         tokenizer = ctranslate2_weights[model_type]["tokenizer"]
-        weight_path = os.path.join(path, "weights", "ctranslate2", directory_name)
-        tokenizer_path = os.path.join(path, "weights", "ctranslate2", directory_name, "tokenizer")
+        weight_path = os_path.join(path, "weights", "ctranslate2", directory_name)
+        tokenizer_path = os_path.join(path, "weights", "ctranslate2", directory_name, "tokenizer")
         self.ctranslate2_translator = ctranslate2.Translator(
             weight_path,
             device="cpu",
@@ -45,9 +48,8 @@ class Translator():
         try:
             self.ctranslate2_tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer, cache_dir=tokenizer_path)
         except Exception as e:
-            import json
-            print(json.dumps({"status":348, "log": f"error:changeCTranslate2Model() {e}"}), flush=True)
-            tokenizer_path = os.path.join("./weights", "ctranslate2", directory_name, "tokenizer")
+            printLog("error:changeCTranslate2Model()", e)
+            tokenizer_path = os_path.join("./weights", "ctranslate2", directory_name, "tokenizer")
             self.ctranslate2_tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer, cache_dir=tokenizer_path)
         self.is_loaded_ctranslate2_model = True
 

src-python/models/translation/translation_utils.py

@@ -2,6 +2,9 @@ import tempfile
 from zipfile import ZipFile
 from os import path as os_path
 from os import makedirs as os_makedirs
+import sys
+sys.path.append(os_path.join(os_path.dirname(__file__), "..", ".."))
+from utils import printLog
 from requests import get as requests_get
 from typing import Callable
 import hashlib
@@ -75,14 +78,14 @@ def downloadCTranslate2Weight(root, weight_type="Small", callbackFunc=None):
             file_size = int(res.headers.get('content-length', 0))
             total_chunk = 0
             with open(os_path.join(tmp_path, filename), 'wb') as file:
-                for chunk in res.iter_content(chunk_size=1024*5):
+                for chunk in res.iter_content(chunk_size=1024*2000):
                     file.write(chunk)
                     if isinstance(callbackFunc, Callable):
                         total_chunk += len(chunk)
                         callbackFunc(total_chunk/file_size)
+                    printLog(f"Downloading {filename}: {total_chunk/file_size:.0%}")
 
             with ZipFile(os_path.join(tmp_path, filename)) as zf:
                 zf.extractall(path)
     except Exception as e:
-        import json
-        print(json.dumps({"status":348, "log": f"error:downloadCTranslate2Weight() {e}"}), flush=True)
+        printLog("error:downloadCTranslate2Weight()", e)