diff --git a/.riahub/workflows/train.yaml b/.riahub/workflows/train.yaml
index 5dd78ed..603ad9c 100644
--- a/.riahub/workflows/train.yaml
+++ b/.riahub/workflows/train.yaml
@@ -371,4 +371,4 @@ jobs:
             ${{ env.WAVESFM_OUTPUT_DIR }}/best.pth
             ${{ env.WAVESFM_OUTPUT_DIR }}/log.txt
           if-no-files-found: warn
-# committed at 2026-05-28T05:41:59.835552+00:00
+# re-committed at 2026-05-28T05:48:03+00:00 with adapter script
diff --git a/scripts/adapt_dataset.py b/scripts/adapt_dataset.py
new file mode 100644
index 0000000..43d3aa6
--- /dev/null
+++ b/scripts/adapt_dataset.py
@@ -0,0 +1,127 @@
+"""Convert RIA Hub HDF5 datasets to WavesFM training format.
+
+Usage:
+    python scripts/adapt_dataset.py input.h5 output.h5
+
+Input format (RIA Hub):
+    /data              complex64 (N, C, T) or (N, T)
+    /metadata/metadata structured array with "label" column
+    file attrs:        data_modality (optional)
+
+Output format (WavesFM):
+    /sample            float32 (N, 2, C, T) -- split I/Q, z-score normalized
+    /label             int64 (N,) -- class indices
+    file attrs:        mu (JSON), std (JSON), labels (JSON str), class_weights (np array), sample_len (int)
+
+WavesFM reads labels via json.loads(attrs["labels"]) and class_weights via
+torch.as_tensor(attrs["class_weights"], dtype=torch.float32). These two attributes
+use different storage formats intentionally.
+"""
+
+import argparse
+import json
+import logging
+import sys
+
+import h5py
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+def adapt_iq(src, dst):
+    """Convert complex64 IQ to float32 split I/Q with z-score normalization.
+
+    Note: loads the full dataset into memory. For typical RIA Hub datasets
+    (< 2 GB) this is fine; chunked I/O can be added if larger inputs arise.
+    """
+    data = src["/data"][:]  # complex64 (N, C, T) or (N, T)
+    if data.ndim not in (2, 3):
+        raise ValueError(f"Expected 2D (N, T) or 3D (N, C, T) data, got {data.ndim}D shape {data.shape}")
+    if data.shape[0] == 0:
+        raise ValueError("Dataset is empty (N=0)")
+    if data.ndim == 2:
+        data = data[:, np.newaxis, :]  # (N, 1, T)
+
+    # Split complex -> (N, 2, C, T) float32
+    # Axis 1 = [real, imag], confirmed by Ahmed: "instead of T complex numbers, 2T real numbers"
+    iq = np.stack([data.real, data.imag], axis=1).astype(np.float32)
+
+    # Per-channel z-score: mean/std over (N, T) for each of 2 I/Q components x C channels.
+    # Explicit float32 casts because numpy < 2.0 returns float64 from .mean()/.std()
+    # on float32 inputs.
+    mu = iq.mean(axis=(0, 3), keepdims=True).astype(np.float32)  # (1, 2, C, 1)
+    std = iq.std(axis=(0, 3), keepdims=True).astype(np.float32)
+    std = np.where(std < 1e-6, np.float32(1.0), std)  # floor preserving float32
+    iq_normed = (iq - mu) / std
+
+    dst.create_dataset("sample", data=iq_normed, compression="gzip", compression_opts=1, shuffle=True)
+    _write_labels(src, dst)
+    _write_stats(dst, mu, std, iq_normed.shape[-1])
+
+
+def _write_labels(src, dst):
+    """Extract labels from metadata, encode to int64 indices, write class_weights."""
+    if "/metadata/metadata" not in src:
+        raise ValueError("Required dataset '/metadata/metadata' not found in input file")
+    meta = src["/metadata/metadata"]
+    if not hasattr(meta.dtype, "names") or meta.dtype.names is None or "label" not in meta.dtype.names:
+        raise ValueError("Required 'label' column not found in '/metadata/metadata'")
+    raw = meta["label"][:]
+    expected = src["/data"].shape[0]
+    if len(raw) != expected:
+        raise ValueError(f"Label count {len(raw)} does not match sample count {expected}")
+
+    # Decode bytes/strings to Python strings
+    if raw.dtype.kind in ("S", "U", "O"):
+        decoded = [v.decode("utf-8", errors="replace") if isinstance(v, bytes) else str(v) for v in raw]
+    else:
+        decoded = [str(v) for v in raw]
+
+    # Label encoding: sorted unique -> 0-based contiguous indices
+    unique_labels = sorted(set(decoded))
+    label_map = {name: idx for idx, name in enumerate(unique_labels)}
+    indices = np.array([label_map[v] for v in decoded], dtype=np.int64)
+
+    dst.create_dataset("label", data=indices)
+    # WavesFM reads: json.loads(h5.attrs["labels"])
+    dst.attrs["labels"] = json.dumps(unique_labels)
+
+    # Inverse-frequency class weights as numpy array (NOT JSON).
+    # WavesFM reads: torch.as_tensor(h5.attrs["class_weights"], dtype=torch.float32)
+    counts = np.bincount(indices, minlength=len(unique_labels)).astype(np.float64)
+    weights = np.where(counts > 0, 1.0 / counts, 0.0)
+    if weights.sum() > 0:
+        weights = weights / weights.sum()
+    dst.attrs["class_weights"] = weights.astype(np.float32)
+
+
+def _write_stats(dst, mu, std, sample_len):
+    """Write normalization stats as file attributes (metadata, not consumed by WavesFM loader)."""
+    dst.attrs["mu"] = json.dumps(mu.flatten().tolist())
+    dst.attrs["std"] = json.dumps(std.flatten().tolist())
+    dst.attrs["sample_len"] = int(sample_len)
+
+
+def adapt(src_path, dst_path):
+    """Main entry point: read RIA Hub HDF5, write WavesFM HDF5."""
+    with h5py.File(src_path, "r") as src:
+        if "/data" not in src:
+            logger.error("Missing required '/data' dataset in source file: %s", src_path)
+            sys.exit(1)
+        if src["/data"].dtype.kind != "c":
+            logger.error("Expected complex64 IQ data, got dtype=%s", src["/data"].dtype)
+            sys.exit(1)
+        with h5py.File(dst_path, "w") as dst:
+            adapt_iq(src, dst)
+
+    logger.info("Adapted %s -> %s", src_path, dst_path)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+    parser = argparse.ArgumentParser(description="Convert RIA Hub HDF5 to WavesFM format")
+    parser.add_argument("input", help="Source HDF5 (RIA Hub format)")
+    parser.add_argument("output", help="Destination HDF5 (WavesFM format)")
+    args = parser.parse_args()
+    adapt(args.input, args.output)