chore: backup before migration to new repo

scripts/asrx_processor.py

@@ -22,6 +22,7 @@ def process_asrx(video_path: str, output_path: str, uuid: str = ""):
 
     try:
         import whisperx
+        import torch
     except ImportError:
         if publisher:
             publisher.error("asrx", "whisperx not installed")
@@ -36,6 +37,14 @@ def process_asrx(video_path: str, output_path: str, uuid: str = ""):
         publisher.info("asrx", "ASRX_LOADING_MODEL")
 
     try:
+        # Fix for PyTorch 2.6+ compatibility
+        # Allow omegaconf types in torch.load
+        import omegaconf
+
+        torch.serialization.add_safe_globals(
+            [omegaconf.listconfig.ListConfig, omegaconf.dictconfig.DictConfig]
+        )
+
         # Load model - using faster-whisper for better performance
         # You can also use: "large-v3", "medium", "small", "base", "tiny"
         model = whisperx.load_model("base", device="cpu", compute_type="int8")
@@ -54,9 +63,14 @@ def process_asrx(video_path: str, output_path: str, uuid: str = ""):
 
         # Diarization (speaker segmentation)
         try:
-            import whisperx
+            from whisperx.diarize import DiarizationPipeline
 
-            diarize_model = whisperx.DiarizationPipeline(use_auth_token=None)
+            # DiarizationPipeline parameters: model_name, token, device, cache_dir
+            diarize_model = DiarizationPipeline(
+                model_name="pyannote/speaker-diarization",
+                token=None,  # HuggingFace token (None for public models)
+                device="cpu",
+            )
             diarize_segments = diarize_model(video_path)
 
             # Assign speaker labels