feat: cache pre-generated LoRA mels to disk for reuse

LoRA mel pre-generation runs a full ODE+CFG for every clip, which is slow.
Cache results to a .pt file next to the output, keyed by a SHA-256 hash
of the LoRA adapter content + generation parameters (seed, steps, CFG,
duration, sample rate, npz file list). Automatically reused on subsequent
runs when parameters haven't changed.

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

nodes/selva_bigvgan_trainer.py

@@ -27,6 +27,8 @@ BigVGAN checkpoint so it can be loaded with SelVA BigVGAN Loader.
 """
 
 import copy
+import hashlib
+import json as _json
 import random
 import threading
 from pathlib import Path
@@ -373,9 +375,34 @@ def _find_audio_for_npz(npz_path: Path):
     return None
 
 
+def _lora_mel_cache_key(lora_adapter_path, data_dir, seed, num_steps,
+                        cfg_strength, duration, sample_rate):
+    """Build a deterministic hash from all parameters that affect LoRA mel generation."""
+    # Hash the LoRA adapter file content (not path — same file moved = same cache)
+    h = hashlib.sha256()
+    with open(lora_adapter_path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    lora_hash = h.hexdigest()[:16]
+
+    # Hash the sorted .npz file list (names only — content is deterministic per name)
+    npz_names = sorted(p.name for p in Path(data_dir).glob("*.npz"))
+
+    key_data = _json.dumps({
+        "lora_hash": lora_hash,
+        "npz_files": npz_names,
+        "seed": seed,
+        "num_steps": num_steps,
+        "cfg_strength": cfg_strength,
+        "duration": duration,
+        "sample_rate": sample_rate,
+    }, sort_keys=True)
+    return hashlib.sha256(key_data.encode()).hexdigest()[:20]
+
+
 def _pregenerate_lora_mels(model, data_dir, lora_adapter_path, device, dtype,
                            sample_rate, duration, seed=42, num_steps=25,
-                           cfg_strength=4.5):
+                           cfg_strength=4.5, cache_dir=None):
     """Generate LoRA mels for all clips with matching audio in data_dir.
 
     Uses the LoRA adapter to run full ODE generation with CFG → VAE decode →
@@ -383,8 +410,26 @@ def _pregenerate_lora_mels(model, data_dir, lora_adapter_path, device, dtype,
     default (4.5) so the degraded mels the vocoder trains on are representative
     of what it will see at inference time.
 
+    If cache_dir is provided, results are cached to disk and reused when
+    generation parameters haven't changed.
+
     Returns list of (mel [n_mels, T_mel], audio [L]) CPU tensors.
     """
+    # ── Check cache ──────────────────────────────────────────────────────────
+    cache_path = None
+    if cache_dir is not None:
+        cache_key = _lora_mel_cache_key(
+            lora_adapter_path, data_dir, seed, num_steps,
+            cfg_strength, duration, sample_rate,
+        )
+        cache_path = Path(cache_dir) / f"lora_mels_{cache_key}.pt"
+        if cache_path.exists():
+            print(f"[BigVGAN] Loading cached LoRA mels: {cache_path.name}", flush=True)
+            cached = torch.load(str(cache_path), map_location="cpu", weights_only=True)
+            pairs = [(m, a) for m, a in zip(cached["mels"], cached["audios"])]
+            print(f"[BigVGAN] Loaded {len(pairs)} cached mel/audio pairs", flush=True)
+            return pairs
+
     from selva_core.model.lora import apply_lora, load_lora
     from selva_core.model.flow_matching import FlowMatching
 
@@ -524,6 +569,16 @@ def _pregenerate_lora_mels(model, data_dir, lora_adapter_path, device, dtype,
         soft_empty_cache()
 
     print(f"[BigVGAN] Pre-generated {len(pairs)} LoRA mel / clean audio pairs", flush=True)
+
+    # ── Save cache ───────────────────────────────────────────────────────────
+    if cache_path is not None and pairs:
+        cache_path.parent.mkdir(parents=True, exist_ok=True)
+        torch.save({
+            "mels":   [m for m, _ in pairs],
+            "audios": [a for _, a in pairs],
+        }, str(cache_path))
+        print(f"[BigVGAN] Cached LoRA mels: {cache_path.name}", flush=True)
+
     return pairs
 
 
@@ -756,6 +811,7 @@ class SelvaBigvganTrainer:
                         model, data_dir, str(lora_path),
                         device, dtype, sample_rate,
                         seq_cfg.duration, seed=seed,
+                        cache_dir=out_path.parent,
                     )
                     if not lora_mel_pairs:
                         raise RuntimeError(