feat(bigvgan-trainer): add eval samples at checkpoints and end

Saves baseline.wav (ground truth roundtrip before training), stepN.wav
at each save_every checkpoint, and final.wav after training completes.
All use the same fixed reference segment (clip 0, position 0) for
direct comparison across checkpoints.

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

nodes/selva_bigvgan_trainer.py

@@ -175,6 +175,27 @@ class SelvaBigvganTrainer:
         torch.manual_seed(seed)
         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]
+
+        def _save_sample(label):
+            """Vocode the reference mel and save as .wav."""
+            try:
+                with torch.no_grad():
+                    wav = vocoder(ref_mel)          # [1, 1, T] or [1, T]
+                    if wav.dim() == 2:
+                        wav = wav.unsqueeze(1)
+                    wav = wav.float().cpu().clamp(-1, 1)
+                wav_path = out_path.parent / f"{out_path.stem}_{label}.wav"
+                torchaudio.save(str(wav_path), wav.squeeze(0), sample_rate)
+                print(f"[BigVGAN] Sample saved: {wav_path}", flush=True)
+            except Exception as e:
+                print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True)
+
+        # Baseline: ground truth roundtrip before any fine-tuning
+        _save_sample("baseline")
+
         pbar = comfy.utils.ProgressBar(steps)
 
         try:
@@ -230,6 +251,9 @@ class SelvaBigvganTrainer:
                             step_path = out_path.parent / f"{out_path.stem}_step{step+1}{out_path.suffix}"
                             torch.save({"generator": vocoder.state_dict()}, str(step_path))
                             print(f"[BigVGAN] Checkpoint: {step_path}", flush=True)
+                            vocoder.eval()
+                            _save_sample(f"step{step+1}")
+                            vocoder.train()
 
         finally:
             vocoder.requires_grad_(False)
@@ -240,4 +264,5 @@ class SelvaBigvganTrainer:
 
         torch.save({"generator": vocoder.state_dict()}, str(out_path))
         print(f"\n[BigVGAN] Saved: {out_path}", flush=True)
+        _save_sample("final")
         return (str(out_path),)