diff --git a/nodes/selva_lora_trainer.py b/nodes/selva_lora_trainer.py
index 2fa6d78..9da3277 100644
--- a/nodes/selva_lora_trainer.py
+++ b/nodes/selva_lora_trainer.py
@@ -1,6 +1,7 @@
 import copy
 import json
 import random
+import traceback
 from pathlib import Path
 
 import numpy as np
@@ -291,12 +292,13 @@ class SelvaLoraTrainer:
                 "Run SelVA Model Loader first to auto-download weights."
             )
         print("[LoRA Trainer] Loading VAE encoder...", flush=True)
+        # Keep VAE in float32: mel_converter uses torch.stft which requires float32 input.
         vae_utils = FeaturesUtils(
             tod_vae_ckpt=str(vae_path),
             enable_conditions=False,
             mode=mode,
             need_vae_encoder=True,
-        ).to(device, dtype).eval()
+        ).to(device).eval()
 
         # --- Pre-load dataset ---
         npz_files = sorted(data_dir.glob("*.npz"))
@@ -324,9 +326,9 @@ class SelvaLoraTrainer:
             try:
                 audio = _load_audio(audio_path, seq_cfg.sampling_rate, seq_cfg.duration)
 
-                # Audio → latent via VAE
+                # Audio → latent via VAE (float32: mel_converter/stft require float32)
                 # encode_audio is @inference_mode — .clone() exits inference mode
-                audio_b = audio.unsqueeze(0).to(device, dtype)
+                audio_b = audio.unsqueeze(0).to(device)
                 dist = vae_utils.encode_audio(audio_b)
                 x1   = dist.mode().clone().cpu()
 
@@ -336,6 +338,7 @@ class SelvaLoraTrainer:
                 dataset.append((x1, bundle["clip_features"], bundle["sync_features"], text_clip))
             except Exception as e:
                 print(f"  [LoRA Trainer] Warning: failed {npz_path.name}: {e}", flush=True)
+                traceback.print_exc()
 
             pbar_load.update(1)