fix(bigvgan): add 44k/BigVGANv2 support to trainer and loader

44k variants use BigVGANv2 directly as the vocoder (no wrapper, no
@inference_mode decorator), accessible at feature_utils.tod.vocoder.
16k wraps BigVGANVocoder inside BigVGAN, accessed at .vocoder.vocoder.
Both trainer and loader now branch on model["mode"].

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

nodes/selva_bigvgan_loader.py

@@ -22,8 +22,9 @@ class SelvaBigvganLoader:
     RETURN_NAMES  = ("model",)
     OUTPUT_TOOLTIPS = ("SELVA_MODEL with the fine-tuned BigVGAN vocoder injected.",)
     DESCRIPTION = (
-        "Loads a fine-tuned BigVGAN vocoder checkpoint from SelVA BigVGAN Trainer "
+        "Loads a fine-tuned BigVGAN/BigVGANv2 vocoder checkpoint from SelVA BigVGAN Trainer "
-        "and replaces the vocoder weights in the SELVA_MODEL. "
+        "and replaces the vocoder weights in the SELVA_MODEL in-place. "
+        "Supports both 16k and 44k models. "
         "Connect the output to SelVA Sampler instead of the base model loader."
     )
 
@@ -47,16 +48,18 @@ class SelvaBigvganLoader:
         if not p.exists():
             raise FileNotFoundError(f"[BigVGAN] Checkpoint not found: {p}")
 
-        if model["mode"] != "16k":
-            raise NotImplementedError(
-                "[BigVGAN] Fine-tuned loader only supports 16k mode."
-            )
-
         ckpt = torch.load(str(p), map_location="cpu", weights_only=False)
         if "generator" not in ckpt:
             raise ValueError(f"[BigVGAN] Expected {{'generator': ...}} in checkpoint, got keys: {list(ckpt.keys())}")
 
-        vocoder = model["feature_utils"].tod.vocoder.vocoder
+        mode = model["mode"]
+        if mode == "16k":
+            vocoder = model["feature_utils"].tod.vocoder.vocoder  # BigVGANVocoder
+        elif mode == "44k":
+            vocoder = model["feature_utils"].tod.vocoder           # BigVGANv2 directly
+        else:
+            raise ValueError(f"[BigVGAN] Unknown mode: {mode}")
+
         vocoder.load_state_dict(ckpt["generator"])
         vocoder.eval()
 

nodes/selva_bigvgan_trainer.py

@@ -63,7 +63,8 @@ class SelvaBigvganTrainer:
     DESCRIPTION = (
         "Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips using "
         "spectral losses (mel L1 + multi-resolution STFT L1). DiT and VAE stay frozen. "
-        "16k mode only. Load the result with SelVA BigVGAN Loader."
+        "Supports both 16k (BigVGAN) and 44k (BigVGANv2) models. "
+        "Load the result with SelVA BigVGAN Loader."
     )
 
     @classmethod
@@ -101,20 +102,22 @@ class SelvaBigvganTrainer:
               segment_seconds, save_every, seed):
         import traceback
 
-        if model["mode"] != "16k":
-            raise NotImplementedError(
-                "[BigVGAN] Only 16k mode is supported. "
-                "44k uses BigVGANv2 which requires a different training setup."
-            )
-
         device        = get_device()
+        mode          = model["mode"]
         feature_utils = model["feature_utils"]
         mel_converter = feature_utils.mel_converter
-        sample_rate    = 16_000
         strategy      = model["strategy"]
 
-        # BigVGANVocoder nn.Module — bypass the @inference_mode wrapper on BigVGAN.forward
+        if mode == "16k":
+            # BigVGANVocoder wrapped inside BigVGAN — bypass the @inference_mode on the wrapper
             vocoder     = feature_utils.tod.vocoder.vocoder
+            sample_rate = 16_000
+        elif mode == "44k":
+            # BigVGANv2 is the vocoder directly (no wrapper); no @inference_mode decorator
+            vocoder     = feature_utils.tod.vocoder
+            sample_rate = 44_100
+        else:
+            raise ValueError(f"[BigVGAN] Unknown mode: {mode}")
 
         # Resolve paths
         data_dir = Path(data_dir.strip())