Update custom model files, README, and requirements

asr_pipeline.py

@@ -460,17 +460,12 @@ class ASRPipeline(transformers.AutomaticSpeechRecognitionPipeline):
         return {"tokens": generated_ids, "is_last": is_last}
 
     def postprocess(self, model_outputs, **kwargs) -> dict[str, str]:
-        print(f"[DEBUG postprocess] Called with type: {type(model_outputs)}")
-
         # Handle list of outputs (from chunking)
         if isinstance(model_outputs, list):
-            print(f"[DEBUG postprocess] List with {len(model_outputs)} items")
             model_outputs = model_outputs[0] if model_outputs else {}
 
         tokens = model_outputs.get("tokens")
-        print(f"[DEBUG postprocess] tokens is None: {tokens is None}")
         if tokens is None:
-            print("[DEBUG postprocess] Falling back to super().postprocess()")
             return super().postprocess(model_outputs, **kwargs)
 
         if torch.is_tensor(tokens):