feat: save mel spectrogram PNG alongside each eval sample

Adds _save_spectrogram() using PIL only (no matplotlib). Each _save_sample
call now writes both a .wav and a _spec.png so training progress is visible
without listening. Colour map is blue→green→yellow (viridis-ish), low
frequencies at the bottom.

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

nodes/selva_bigvgan_trainer.py

@@ -23,6 +23,37 @@ import folder_paths
 
 from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
 
+def _save_spectrogram(path, mel_tensor):
+    """Save mel spectrogram [1, n_mels, T] as a PNG using PIL (no matplotlib dep).
+
+    Normalises to [0, 255], flips frequency axis so low freqs are at the bottom,
+    and saves as a greyscale PNG with a simple viridis-like colour map.
+    """
+    try:
+        from PIL import Image
+        import numpy as np
+
+        mel = mel_tensor.squeeze(0).float().cpu().numpy()  # [n_mels, T]
+        mel = mel[::-1]                                    # low freq at bottom
+        lo, hi = mel.min(), mel.max()
+        if hi > lo:
+            mel = (mel - lo) / (hi - lo)
+        else:
+            mel = mel - lo
+        img_u8 = (mel * 255).clip(0, 255).astype(np.uint8)
+
+        # Simple blue→green→yellow colour map (viridis-ish) via LUT
+        lut_r = np.array([int(max(0, min(255, 255 * (v * 2 - 1)))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
+        lut_g = np.array([int(max(0, min(255, 255 * (1 - abs(v * 2 - 1))))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
+        lut_b = np.array([int(max(0, min(255, 255 * (1 - v * 2)))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
+        r = Image.fromarray(lut_r[img_u8])
+        g = Image.fromarray(lut_g[img_u8])
+        b = Image.fromarray(lut_b[img_u8])
+        Image.merge("RGB", (r, g, b)).save(str(path))
+    except Exception as e:
+        print(f"[BigVGAN] Spectrogram save failed: {e}", flush=True)
+
+
 def _save_wav(path, wav_tensor, sample_rate):
     """Save [channels, samples] float32 tensor to .wav.
 
@@ -288,8 +319,13 @@ def _do_train(vocoder, mel_converter, clips,
                     wav = wav.unsqueeze(1)
                 wav = wav.float().cpu().clamp(-1, 1)
             wav_path  = out_path.parent / f"{out_path.stem}_{label}.wav"
+            spec_path = out_path.parent / f"{out_path.stem}_{label}_spec.png"
             _save_wav(wav_path, wav.squeeze(0), sample_rate)
-            print(f"[BigVGAN] Sample saved: {wav_path}", flush=True)
+            # Compute mel of the vocoded output for visual comparison
+            with torch.no_grad():
+                pred_mel = mel_converter(wav.squeeze(1).to(mel_converter.mel_basis.device))
+            _save_spectrogram(spec_path, pred_mel)
+            print(f"[BigVGAN] Sample saved: {wav_path}  spec: {spec_path}", flush=True)
         except Exception as e:
             print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True)