cleanup: remove dead code and duplicate docs

- Remove session-ses_2f27.md (161KB raw session log)
- Remove 49 ROOT_* duplicate files across REFERENCE/
- Remove 14 duplicate files between REFERENCE/ root and history/
- Remove asr_legacy.rs (dead code, replaced by asr.rs)
- Remove src/core/worker/ (duplicate JobWorker)
- Remove src/core/layers/ (empty directory)
- Remove 4 .bak files in src/
- Remove 7 dead private methods in worker/processor.rs
- Remove backup directory from git tracking

scripts/audio_taxonomy_processor.py

@@ -4,7 +4,6 @@ Audio Taxonomy Processor (Hugging Face Transformers)
 職責：使用 AST 模型進行高精度音頻分類，並映射到業務分類。
 """
 
-import numpy as np
 import json
 import os
 import sys
@@ -75,7 +74,7 @@ def map_to_taxonomy(predictions):
 
 def run_audio_taxonomy(audio_path, chunk_sec=1.0, hop_sec=0.5):
     """執行分類"""
-    print(f"🔍 Loading AST model (MIT) from Hugging Face...")
+    print("🔍 Loading AST model (MIT) from Hugging Face...")
     # 使用 Audio Spectrogram Transformer，準確率高且支援 MPS/CPU
     classifier = pipeline(
         "audio-classification",
@@ -103,7 +102,7 @@ def run_audio_taxonomy(audio_path, chunk_sec=1.0, hop_sec=0.5):
 
             if taxonomy:
                 results.append({"timestamp": round(current, 1), "categories": taxonomy})
-        except Exception as e:
+        except Exception:
             pass  # 跳過錯誤片段
 
         current += hop_sec
@@ -132,6 +131,6 @@ if __name__ == "__main__":
     with open(OUTPUT_JSON, "w", encoding="utf-8") as f:
         json.dump({"audio_taxonomy": events}, f, indent=2, ensure_ascii=False)
 
-    print(f"\n🎉 Classification Complete!")
+    print("\n🎉 Classification Complete!")
     print(f"✅ Found {len(events)} tagged audio segments.")
     print(f"💾 Saved to {OUTPUT_JSON}")