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/asrx_self/speaker_cluster.py

@@ -138,7 +138,7 @@ def spectral_clustering_speaker(
 
         speaker_labels = clustering.fit_predict(similarity_matrix)
 
-        print(f"[Clustering] Spectral clustering completed")
+        print("[Clustering] Spectral clustering completed")
         print(f"[Clustering] n_speakers: {n_speakers}")
         print(f"[Clustering] n_segments: {n_segments}")
 
@@ -146,7 +146,7 @@ def spectral_clustering_speaker(
 
     except Exception as e:
         print(f"[Clustering] Spectral clustering failed: {e}")
-        print(f"[Clustering] Using fallback: 2 speakers")
+        print("[Clustering] Using fallback: 2 speakers")
         # 簡單分配：前一半是 SPEAKER_0，後一半是 SPEAKER_1
         speaker_labels = np.array(
             [0] * (n_segments // 2) + [1] * (n_segments - n_segments // 2)
@@ -203,7 +203,7 @@ def agglomerative_clustering_speaker(
 
     speaker_labels = clustering.fit_predict(embeddings)
 
-    print(f"[Clustering] Agglomerative clustering completed")
+    print("[Clustering] Agglomerative clustering completed")
     print(f"[Clustering] n_speakers: {n_speakers}")
 
     return speaker_labels, n_speakers
@@ -249,7 +249,6 @@ def compute_diarization_purity(speaker_labels, ground_truth_labels=None):
     """
     if ground_truth_labels is None:
         # 沒有 ground truth，使用聚類純度近似
-        from sklearn.metrics import silhouette_score
 
         # 使用餘弦相似度作為距離
         purity = 0.5  # 預設值
@@ -300,7 +299,7 @@ if __name__ == "__main__":
         similarity, n_speakers=None, auto_estimate=True
     )
 
-    print(f"\n[Test] Clustering results:")
+    print("\n[Test] Clustering results:")
     print(f"  True n_speakers: {n_speakers}")
     print(f"  Estimated n_speakers: {n_clusters}")
     print(f"  Unique labels: {np.unique(labels)}")