add logger function

backend/server.py

@@ -24,7 +24,7 @@
 import io
 
 from modules.whisper.faster_whisper_inference import FasterWhisperInference
-from modules.utils.logger import get_logger
+from modules.utils.logger import get_backend_logger
 from modules.vad.silero_vad import SileroVAD
 from modules.diarize.diarizer import Diarizer
 from modules.diarize.audio_loader import SAMPLE_RATE
@@ -41,7 +41,7 @@
 vad_inferencer = None
 whisper_inferencer = None
 diarization_inferencer = None
-logger = get_logger("Whisper-WebUI-Backend")
+logger = get_backend_logger()
 
 
 def format_stream_result(generator: Generator[dict[str, Any], Any, None]):
@@ -163,10 +163,10 @@ async def diarization(
 async def transcription(
     file: UploadFile = File(default=None, description="Input file for video or audio. This will be pre-processed with ffmpeg in the server"),
     file_url: str = Form(default=None, description="Input file url for video or audio. You need to provide either the file or the file URL, but not both"),
-    response_format: str = Form(default="json"),
-    model_size: str = Form(default="large-v2"),
-    language: Optional[str] = Form(default=None),
-    task: str = Form(default="transcribe"),
+    response_format: str = Form(default="json", description="Response format between [\"json\", \"stream\"]"),
+    model_size: str = Form(default="large-v2", description="Model sizes for faster-whisper."),
+    language: Optional[str] = Form(default=None, description="Source language of the input. Automatically detected if it's not provided."),
+    task: str = Form(default="transcribe", description="Tasks between "),
     beam_size: int = Form(default=5),
     best_of: int = Form(default=5),
     patience: float = Form(default=1),

modules/utils/logger.py

@@ -1,6 +1,14 @@
 import logging
 
 
+def get_backend_logger():
+    return get_logger("Whisper-WebUI-Backend")
+
+
+def get_gradio_logger():
+    return get_logger("Whisper-WebUI")
+
+
 def get_logger(name: str):
     logger = logging.getLogger(name)