diff --git a/nodes/selva_dataset_pipeline.py b/nodes/selva_dataset_pipeline.py
index 2359e89..f360e5d 100644
--- a/nodes/selva_dataset_pipeline.py
+++ b/nodes/selva_dataset_pipeline.py
@@ -110,3 +110,80 @@ class SelvaDatasetResampler:
 
         print(f"[DatasetResampler] {changed}/{len(dataset)} clips resampled → {target_sr} Hz", flush=True)
         return (out,)
+
+
+class SelvaDatasetLUFSNormalizer:
+    """Normalize each clip to a target integrated LUFS level + true peak limit."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "dataset": (AUDIO_DATASET,),
+                "target_lufs": ("FLOAT", {
+                    "default": -23.0, "min": -40.0, "max": -6.0, "step": 0.5,
+                    "tooltip": "Target integrated loudness in LUFS. -23 is EBU R128 standard.",
+                }),
+                "true_peak_dbtp": ("FLOAT", {
+                    "default": -1.0, "min": -6.0, "max": 0.0, "step": 0.5,
+                    "tooltip": "True peak ceiling in dBTP. Applied after LUFS gain.",
+                }),
+            }
+        }
+
+    RETURN_TYPES  = (AUDIO_DATASET,)
+    RETURN_NAMES  = ("dataset",)
+    FUNCTION      = "normalize"
+    CATEGORY      = SELVA_CATEGORY
+    DESCRIPTION   = (
+        "Normalize each clip to target_lufs (BS.1770-4) then apply a true peak ceiling. "
+        "Skips clips that are too short for LUFS measurement (< 0.4 s)."
+    )
+
+    def normalize(self, dataset, target_lufs: float, true_peak_dbtp: float):
+        import pyloudnorm as pyln
+
+        tp_linear = 10.0 ** (true_peak_dbtp / 20.0)
+        out = []
+        skipped = 0
+
+        for item in dataset:
+            wav = item["waveform"][0]           # [C, L]
+            sr  = item["sample_rate"]
+
+            # pyloudnorm wants [L] mono or [L, C] multichannel, float64
+            wav_np = wav.permute(1, 0).double().numpy()  # [L, C]
+            if wav_np.shape[1] == 1:
+                wav_np = wav_np[:, 0]           # [L] mono
+
+            meter = pyln.Meter(sr)
+            try:
+                loudness = meter.integrated_loudness(wav_np)
+            except Exception:
+                skipped += 1
+                out.append(item)
+                continue
+
+            if not np.isfinite(loudness):
+                skipped += 1
+                out.append(item)
+                continue
+
+            gain_db     = target_lufs - loudness
+            gain_linear = 10.0 ** (gain_db / 20.0)
+
+            wav_norm = wav * gain_linear
+
+            # True peak limit
+            peak = wav_norm.abs().max().item()
+            if peak > tp_linear:
+                wav_norm = wav_norm * (tp_linear / peak)
+
+            out.append({"waveform": wav_norm.unsqueeze(0), "sample_rate": sr, "name": item["name"]})
+
+        print(
+            f"[LUFSNormalizer] {len(dataset) - skipped}/{len(dataset)} clips normalized  "
+            f"target={target_lufs} LUFS  TP={true_peak_dbtp} dBTP  skipped={skipped}",
+            flush=True,
+        )
+        return (out,)