fix: trim/pad latent to seq_cfg.latent_seq_len before decoding

Without this the decoder produced 7s instead of 8s due to STFT rounding.
Same fix as _prepare_dataset uses for training data.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/selva_vae_roundtrip.py

@@ -96,8 +96,18 @@ class SelvaVaeRoundtrip:
 
             with torch.no_grad():
                 # Encode
-                dist    = vae.encode_audio(wav_b)
-                latent  = dist.mode().clone()              # [1, latent_dim, T]
+                dist   = vae.encode_audio(wav_b)
+                latent = dist.mode().clone()               # [1, latent_dim, T]
+
+                # Trim/pad latent to the exact model sequence length
+                # (same as _prepare_dataset) so the decoder produces the right duration
+                tgt = seq_cfg.latent_seq_len
+                if latent.shape[2] < tgt:
+                    import torch.nn.functional as F
+                    latent = F.pad(latent, (0, tgt - latent.shape[2]))
+                elif latent.shape[2] > tgt:
+                    latent = latent[:, :, :tgt]
+
                 print(f"[VAE Roundtrip] Latent: shape={tuple(latent.shape)}  "
                       f"mean={latent.mean():.4f}  std={latent.std():.4f}", flush=True)