feat: add dataset pipeline nodes + latent augmentation for LoRA trainer

New dataset pipeline nodes:
- SelvaDatasetSpectralMatcher: batch spectral EQ toward VAE distribution
- SelvaDatasetHfSmoother: batch HF attenuation for codec compatibility
- SelvaDatasetAugmenter: gain/pitch/time-stretch variants with npz origin tracking

Improvements:
- Inspector: silence detection (max_silence_fraction param)
- Saver: origin_name lookup for augmented clips' npz pairing
- LoRA trainer: latent_mixup_alpha + latent_noise_sigma regularization
- LoRA trainer: one-time SR mismatch warning in _load_audio

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

nodes/selva_lora_trainer.py

@@ -72,6 +72,10 @@ def _load_audio(path: Path, target_sr: int, duration: float) -> torch.Tensor:
         waveform = waveform.mean(0, keepdim=True)
     waveform = waveform.squeeze(0).float()
     if sr != target_sr:
+        if not getattr(_load_audio, "_sr_warned", False):
+            print(f"[LoRA Trainer] WARNING: audio sr={sr} ≠ target {target_sr}, resampling on-the-fly. "
+                  f"Pre-resample with SelVA Dataset Resampler for faster loading.", flush=True)
+            _load_audio._sr_warned = True
         waveform = torchaudio.functional.resample(
             waveform.unsqueeze(0), sr, target_sr).squeeze(0)
     target_len = int(duration * target_sr)
@@ -557,6 +561,17 @@ class SelvaLoraTrainer:
                                "cosine: decay from lr to ~0 following a cosine curve — "
                                "prevents oscillation when LR is slightly too high.",
                 }),
+                "latent_mixup_alpha": ("FLOAT", {
+                    "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05,
+                    "tooltip": "Beta distribution alpha for latent mixup (MusicLDM, arXiv:2308.01546). "
+                               "0 = disabled. 0.4 recommended. Mixes pairs of training latents "
+                               "to reduce memorization on small datasets.",
+                }),
+                "latent_noise_sigma": ("FLOAT", {
+                    "default": 0.0, "min": 0.0, "max": 0.1, "step": 0.005,
+                    "tooltip": "Additive Gaussian noise on training latents, scaled by x1.std(). "
+                               "0 = disabled. 0.01–0.03 adds mild regularization against overfitting.",
+                }),
             },
         }
 
@@ -581,7 +596,8 @@ class SelvaLoraTrainer:
               grad_accum=1, save_every=500, resume_path="", seed=42,
               timestep_mode="uniform", logit_normal_sigma=1.0, curriculum_switch=0.6,
               lora_dropout=0.0, lora_plus_ratio=1.0,
-              init_mode="pissa", use_rslora=True, lr_schedule="constant"):
+              init_mode="pissa", use_rslora=True, lr_schedule="constant",
+              latent_mixup_alpha=0.0, latent_noise_sigma=0.0):
 
         torch.manual_seed(seed)
         random.seed(seed)
@@ -633,6 +649,7 @@ class SelvaLoraTrainer:
                 timestep_mode, logit_normal_sigma, curriculum_switch,
                 lora_dropout, lora_plus_ratio, lr_schedule,
                 init_mode, use_rslora,
+                latent_mixup_alpha, latent_noise_sigma,
             )
             return (r["patched_model"], r["adapter_path"], r["loss_curve"])
 
@@ -645,6 +662,7 @@ class SelvaLoraTrainer:
         timestep_mode="uniform", logit_normal_sigma=1.0, curriculum_switch=0.6,
         lora_dropout=0.0, lora_plus_ratio=1.0, lr_schedule="constant",
         init_mode="pissa", use_rslora=True,
+        latent_mixup_alpha=0.0, latent_noise_sigma=0.0,
     ):
         # --- Prepare generator copy with LoRA ---
         generator = copy.deepcopy(model["generator"]).to(device, dtype)
@@ -748,6 +766,8 @@ class SelvaLoraTrainer:
             "lr_schedule":         lr_schedule,
             "init_mode":           init_mode,
             "use_rslora":          use_rslora,
+            "latent_mixup_alpha":  latent_mixup_alpha,
+            "latent_noise_sigma":  latent_noise_sigma,
         }
 
         # For curriculum mode: compute the step at which we switch from logit_normal to uniform
@@ -775,6 +795,18 @@ class SelvaLoraTrainer:
 
                 x1 = generator.normalize(x1)
 
+                # Latent mixup (MusicLDM, arXiv:2308.01546)
+                if latent_mixup_alpha > 0 and x1.shape[0] > 1:
+                    lam = torch.distributions.Beta(
+                        latent_mixup_alpha, latent_mixup_alpha
+                    ).sample().to(device)
+                    idx = torch.randperm(x1.shape[0], device=device)
+                    x1 = lam * x1 + (1 - lam) * x1[idx]
+
+                # Latent noise injection
+                if latent_noise_sigma > 0:
+                    x1 = x1 + torch.randn_like(x1) * latent_noise_sigma * x1.std()
+
                 if timestep_mode == "logit_normal" or (
                     timestep_mode == "curriculum" and step <= curriculum_switch_step
                 ):