fix(bigvgan-trainer): cast audio to model dtype to match bf16 mel_converter buffers

Model loaded in bf16 causes mel_basis buffer to be bf16. Audio loaded
from disk is float32, causing matmul dtype mismatch. Cast all audio
tensors to model["dtype"] before passing to mel_converter/vocoder.

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

nodes/selva_bigvgan_trainer.py

@@ -121,6 +121,7 @@ class SelvaBigvganTrainer:
 
         device        = get_device()
         mode          = model["mode"]
+        dtype         = model["dtype"]   # bf16/fp16/fp32 — must match mel_converter buffers
         feature_utils = model["feature_utils"]
         mel_converter = feature_utils.mel_converter
         strategy      = model["strategy"]
@@ -193,8 +194,8 @@ class SelvaBigvganTrainer:
         random.seed(seed)
 
         # Fixed reference segment for eval samples — always clip 0, start 0
-        ref_clip = clips[0][:segment_samples].to(device)  # [T]
-        ref_mel  = mel_converter(ref_clip.unsqueeze(0))    # [1, n_mels, T_mel]
+        ref_clip = clips[0][:segment_samples].to(device, dtype)  # [T]
+        ref_mel  = mel_converter(ref_clip.unsqueeze(0))           # [1, n_mels, T_mel]
 
         def _save_sample(label):
             """Vocode the reference mel and save as .wav."""
@@ -231,7 +232,7 @@ class SelvaBigvganTrainer:
                             start = random.randint(0, clip.shape[0] - segment_samples)
                             batch.append(clip[start : start + segment_samples])
 
-                        target_flat = torch.stack(batch).to(device)          # [B, T]
+                        target_flat = torch.stack(batch).to(device, dtype)   # [B, T]
                         target_wav  = target_flat.unsqueeze(1)                # [B, 1, T]
 
                         # Fixed target mel (no grad needed here)