diff --git a/nodes/selva_dataset_pipeline.py b/nodes/selva_dataset_pipeline.py
index d1d78dd..2359e89 100644
--- a/nodes/selva_dataset_pipeline.py
+++ b/nodes/selva_dataset_pipeline.py
@@ -66,3 +66,47 @@ class SelvaDatasetLoader:
 
         print(f"[DatasetLoader] Loaded {len(dataset)} clips from {folder}", flush=True)
         return (dataset,)
+
+
+class SelvaDatasetResampler:
+    """Resample all clips in a dataset to a target sample rate using soxr VHQ."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "dataset": (AUDIO_DATASET,),
+                "target_sr": ("INT", {
+                    "default": 44100, "min": 8000, "max": 192000,
+                    "tooltip": "Target sample rate. 44100 for large SelVA model, 16000 for small.",
+                }),
+            }
+        }
+
+    RETURN_TYPES  = (AUDIO_DATASET,)
+    RETURN_NAMES  = ("dataset",)
+    FUNCTION      = "resample"
+    CATEGORY      = SELVA_CATEGORY
+    DESCRIPTION   = "Resample all clips to target_sr using soxr VHQ. Skips clips already at target rate."
+
+    def resample(self, dataset, target_sr: int):
+        import soxr
+
+        out = []
+        changed = 0
+        for item in dataset:
+            sr = item["sample_rate"]
+            if sr == target_sr:
+                out.append(item)
+                continue
+
+            wav = item["waveform"][0]               # [C, L]
+            # soxr expects [L, C] (time-first), float64
+            wav_np = wav.permute(1, 0).double().numpy()   # [L, C]
+            wav_rs = soxr.resample(wav_np, sr, target_sr, quality="VHQ")
+            wav_t  = torch.from_numpy(wav_rs).float().permute(1, 0).unsqueeze(0)  # [1, C, L]
+            out.append({"waveform": wav_t, "sample_rate": target_sr, "name": item["name"]})
+            changed += 1
+
+        print(f"[DatasetResampler] {changed}/{len(dataset)} clips resampled → {target_sr} Hz", flush=True)
+        return (out,)