modify params

app.py

@@ -948,11 +948,11 @@ class WhisperTranscriber:
             # Step 1: Preprocess per chunk JSON
             print("Preprocessing chunk JSON...")
             pre_meta = self.preprocess_from_task_json(task_json)
-            transcribe_options = pre_meta.get("options", None)
+            #transcribe_options = pre_meta.get("options", None)
             if isinstance(pre_meta, list):
-                return self.transcribe_segments(pre_meta, language, translate, prompt, batch_size, model_name, transcribe_options)
+                return self.transcribe_segments(pre_meta, language, translate, prompt, batch_size, model_name)
             elif isinstance(pre_meta, dict) and "chunk" in pre_meta:
-                return self.transcribe_chunk(pre_meta, language, translate, prompt, batch_size, model_name, transcribe_options)
+                return self.transcribe_chunk(pre_meta, language, translate, prompt, batch_size, model_name)
         except Exception as e:
             import traceback
             traceback.print_exc()
@@ -961,12 +961,13 @@ class WhisperTranscriber:
 
     @spaces.GPU
     def transcribe_chunk(self, pre_meta, language=None, 
-                        translate=False, prompt=None, batch_size=8, model_name: str = DEFAULT_MODEL, transcribe_options: dict = None):
+                        translate=False, prompt=None, batch_size=8, model_name: str = DEFAULT_MODEL):
         """Main processing function with diarization using task JSON for a single chunk.
 
         Transcribes full (preprocessed) audio once, performs diarization, merges speakers into transcription.
         """
         try:
+            transcribe_options = pre_meta.get("options", None)
             print("Transcribing chunk...")
             # Step 1: Preprocess per chunk JSON
             if pre_meta["chunk"].get("skip"):
@@ -1008,7 +1009,7 @@ class WhisperTranscriber:
 
     @spaces.GPU
     def transcribe_segments(self, pre_metas, language=None, 
-                        translate=False, prompt=None, batch_size=8, model_name: str = DEFAULT_MODEL, transcribe_options: dict = None):
+                        translate=False, prompt=None, batch_size=8, model_name: str = DEFAULT_MODEL):
         """Main processing function with diarization using task JSON for a single chunk.
 
         Transcribes full (preprocessed) audio once, performs diarization, merges speakers into transcription.
@@ -1018,6 +1019,7 @@ class WhisperTranscriber:
             transcription_results = []
             # Step 1: Preprocess per chunk JSON
             for pre_meta in pre_metas:
+                transcribe_options = pre_meta.get("options", None)
                 chunk = pre_meta["chunk"]
                 if chunk.get("skip"):
                     return {"segments": [], "language": "unknown", "num_speakers": 0, "transcription_method": "diarized_segments_batched", "batch_size": batch_size}