Update app.py

app.py

@@ -38,13 +38,6 @@ except Exception as e:
     print(f"Error loading pyannote pipeline: {type(e).__name__}: {e}. Diarization will be skipped.")
     diarization_pipeline = None
 global_diarizer = diarization_pipeline
-ALIGN_MODEL_MAP = {
-    "ur": "kingabzpro/wav2vec2-large-xls-r-300m-Urdu",
-    "pa": "kingabzpro/wav2vec2-large-xls-r-300m-Urdu",
-    "sd": "Abdullah104/wav2vec2-large-xls-r-300m-sindhi-kaggle",
-    "ps": "ihanif/wav2vec2-xls-r-300m-pashto",
-}
-global_align_model_cache = {}
 model_name = "large-v3"
 class TimelineItem(BaseModel):
     start: float
@@ -165,7 +158,6 @@ def analyze_audio(audio_file: str,
                   preprocess_params: Optional[Dict[str, Any]] = None) -> AnalysisResults:
     
     results = AnalysisResults()
-    global global_align_model_cache, ALIGN_MODEL_MAP 
     ends: List[float] = []      
     rows: List[Dict[str, Any]] = []
     rawTranscriptionText: str = ""
@@ -210,27 +202,6 @@ def analyze_audio(audio_file: str,
         print(f"Detected language: {languageCode}. Aligning transcription...")
         align_model = None
         metadata = None
-        if language_code not in global_align_model_cache:
-            align_model_name = ALIGN_MODEL_MAP.get(language_code)
-            try:
-                if align_model_name:
-                    print(f"Loading custom alignment model for {language_code}: {align_model_name}...")
-                align_model, metadata = whisperx.load_align_model(
-                    language_code=language_code, 
-                    model_name=align_model_name, 
-                    device=device
-                )
-                global_align_model_cache[language_code] = (align_model, metadata)
-                print(f"Alignment model loaded/cached for language: {language_code}")
-                             
-            except Exception as e:
-                warn(results, "ALIGN_LOAD_FAIL", f"Failed to load alignment model for {language_code}: {e}. Alignment skipped.")
-                global_align_model_cache[language_code] = (None, None) 
-        else:
-            align_model, metadata = global_align_model_cache[language_code]
-            if align_model:
-                 print(f"Alignment model loaded from cache for language: {language_code}")
-                 
         if align_model:
             try:
                 print("Performing word-level alignment...")