diff --git a/audio_separator/models-scores.json b/audio_separator/models-scores.json
index 0e57d06..dde4872 100644
--- a/audio_separator/models-scores.json
+++ b/audio_separator/models-scores.json
@@ -5335,174 +5335,6 @@
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@@ -6533,33 +6365,6 @@
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@@ -10474,62 +10279,6 @@
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@@ -10736,85 +10485,6 @@
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@@ -11021,85 +10691,6 @@
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@@ -12986,7 +12577,27 @@
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+    ],
+    "median_scores": {
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+        "SAR": 11.0359,
+        "ISR": 19.3753
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+        "SIR": 19.5435,
+        "SAR": 9.20801,
+        "ISR": 13.5037
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   "htdemucs.yaml": {
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@@ -13020,7 +12631,27 @@
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       }
-    ]
+    ],
+    "median_scores": {
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+        "SAR": 10.7492,
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+        "ISR": 17.4674
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+        "SIR": 18.7906,
+        "SAR": 8.75809,
+        "ISR": 13.0067
+      }
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   "hdemucs_mmi.yaml": {
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@@ -13054,7 +12685,27 @@
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         }
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-    ]
+    ],
+    "median_scores": {
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+        "SIR": 20.9333,
+        "SAR": 11.0289,
+        "ISR": 19.1138
+      },
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+        "SIR": 23.2568,
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+        "SIR": 18.4985,
+        "SAR": 8.87331,
+        "ISR": 13.3686
+      }
+    }
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   "htdemucs_6s.yaml": {
     "model_name": "Demucs v4: htdemucs_6s",
@@ -13088,7 +12739,27 @@
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         }
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+    "median_scores": {
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+        "SIR": 19.4989,
+        "SAR": 10.6463,
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+        "SAR": 12.997,
+        "ISR": 16.9512
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+        "SAR": 8.85638,
+        "ISR": 13.1591
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+    }
   },
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@@ -14120,33 +13791,6 @@
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@@ -14353,31 +13997,6 @@
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@@ -14584,108 +14203,6 @@
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@@ -15258,111 +14775,6 @@
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@@ -16525,101 +15937,34 @@
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+  "kuielab_a_other.onnx": {
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+    "track_scores": [],
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+    "track_scores": [],
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+  "kuielab_a_drums.onnx": {
+    "model_name": "MDX-Net Model: kuielab_a_drums",
+    "track_scores": [],
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+  "kuielab_b_other.onnx": {
+    "model_name": "MDX-Net Model: kuielab_b_other",
+    "track_scores": [],
+    "median_scores": null
+  },
+  "kuielab_b_bass.onnx": {
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+    "track_scores": [],
+    "median_scores": null
+  },
+  "kuielab_b_drums.onnx": {
+    "model_name": "MDX-Net Model: kuielab_b_drums",
+    "track_scores": [],
     "median_scores": null
   }
 }
\ No newline at end of file
diff --git a/tests/model-metrics/test-all-models.py b/tests/model-metrics/test-all-models.py
index 4b805a7..6ed7897 100644
--- a/tests/model-metrics/test-all-models.py
+++ b/tests/model-metrics/test-all-models.py
@@ -146,6 +146,19 @@ def calculate_median_scores(track_scores):
     return median_scores if median_scores else None
 
 
+def cleanup_combined_results(combined_results):
+    """Remove models with no valid scores and clean up null entries"""
+    cleaned_results = {}
+    for model, data in combined_results.items():
+        # Filter out null entries from track_scores
+        valid_scores = [score for score in data["track_scores"] if score is not None]
+
+        # Only keep models that have valid scores
+        if valid_scores:
+            cleaned_results[model] = {"model_name": data["model_name"], "track_scores": valid_scores, "median_scores": calculate_median_scores(valid_scores)}
+    return cleaned_results
+
+
 def main():
     logger.info("Starting model evaluation script...")
     os.makedirs(RESULTS_PATH, exist_ok=True)
@@ -166,6 +179,8 @@ def main():
         logger.info("Loading existing combined results...")
         with open(combined_results_path) as f:
             combined_results = json.load(f)
+            # Clean up existing results
+            combined_results = cleanup_combined_results(combined_results)
 
     # Process all tracks in MUSDB18
     for track in mus.tracks:
@@ -204,15 +219,18 @@ def main():
                             if model_results:
                                 combined_results[test_model]["track_scores"].append(model_results)
                             else:
-                                combined_results[test_model]["track_scores"].append(None)
+                                logger.info(f"Skipping model {test_model} for track {track_name} due to no evaluatable stems")
                         except Exception as e:
                             logger.error(f"Error evaluating model {test_model} with track {track_name}: {str(e)}")
-                            combined_results[test_model]["track_scores"].append(None)
                             continue
 
-                    # Calculate and update median scores regardless of whether track was evaluated
-                    median_scores = calculate_median_scores(combined_results[test_model]["track_scores"])
-                    combined_results[test_model]["median_scores"] = median_scores
+                    # Only calculate and update median scores if there are any track scores
+                    if combined_results[test_model]["track_scores"]:
+                        median_scores = calculate_median_scores(combined_results[test_model]["track_scores"])
+                        combined_results[test_model]["median_scores"] = median_scores
+                    else:
+                        # Remove the model entry if there are no track scores
+                        del combined_results[test_model]
 
                     # Save results after each model (whether evaluated or skipped)
                     os.makedirs(os.path.dirname(combined_results_path), exist_ok=True)