feat: add reference audio to LoRA evaluator

Loads the first clip's original audio (same clip used for inference),
copies it to output_dir/reference.wav, runs spectral metrics and
saves a spectrogram. Appears first in the comparison chart so generated
samples can be judged against the target sound.

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

nodes/selva_lora_evaluator.py

@@ -38,6 +38,8 @@ from .selva_lora_trainer import (
     _spectral_metrics,
     _save_spectrogram,
     _pil_to_tensor,
+    _find_audio,
+    _load_audio,
 )
 from selva_core.model.lora import apply_lora, load_lora
 
@@ -199,7 +201,31 @@ class SelvaLoraEvaluator:
         seq_cfg            = model["seq_cfg"]
 
         # ------------------------------------------------------------------
-        # 3. Build summary skeleton
+        # 3. Load reference audio (first clip, same one used for eval samples)
+        # ------------------------------------------------------------------
+        first_npz   = sorted(data_dir.glob("*.npz"))[0]
+        audio_path  = _find_audio(first_npz)
+        ref_record  = {"id": "reference", "path": str(audio_path) if audio_path else None,
+                       "wav_path": None, "spectrogram_path": None,
+                       "spectral_metrics": None, "status": "failed"}
+        if audio_path:
+            try:
+                ref_wav = _load_audio(audio_path, seq_cfg.sampling_rate, seq_cfg.duration)
+                ref_wav = ref_wav.unsqueeze(0)   # [1, L]
+                ref_out = output_dir / "reference.wav"
+                import shutil
+                shutil.copy2(str(audio_path), str(ref_out))
+                ref_record["wav_path"]         = str(ref_out)
+                ref_record["spectral_metrics"] = _spectral_metrics(ref_wav, seq_cfg.sampling_rate)
+                _save_spectrogram(ref_wav, seq_cfg.sampling_rate, output_dir / "reference")
+                ref_record["spectrogram_path"] = str((output_dir / "reference").with_suffix(".png"))
+                ref_record["status"] = "completed"
+                print(f"[LoRA Evaluator] Reference: {audio_path.name}", flush=True)
+            except Exception as e:
+                print(f"[LoRA Evaluator] Reference load failed: {e}", flush=True)
+
+        # ------------------------------------------------------------------
+        # 4. Build summary skeleton
         # ------------------------------------------------------------------
         summary = {
             "name":         name,
@@ -207,9 +233,10 @@ class SelvaLoraEvaluator:
             "completed_at": None,
             "data_dir":     str(data_dir),
             "output_dir":   str(output_dir),
+            "reference":    first_npz.name,
             "steps":        steps,
             "seed":         seed,
-            "adapters":     [],
+            "adapters":     [ref_record],
         }
         summary_path = output_dir / "eval_summary.json"
 
@@ -219,7 +246,7 @@ class SelvaLoraEvaluator:
         _write_summary()
 
         # ------------------------------------------------------------------
-        # 4. Per-adapter evaluation loop
+        # 5. Per-adapter evaluation loop
         # ------------------------------------------------------------------
         pbar = comfy.utils.ProgressBar(len(spec["adapters"]))
 
@@ -351,7 +378,7 @@ class SelvaLoraEvaluator:
         # ------------------------------------------------------------------
         # 6. Comparison chart
         # ------------------------------------------------------------------
-        completed = [r for r in summary["adapters"] if r["status"] == "completed"]
+        completed = [r for r in summary["adapters"] if r.get("status") == "completed"]
         if completed:
             ids          = [r["id"] for r in completed]
             metrics_list = [r["spectral_metrics"] for r in completed]