feat(ti-trainer): generate baseline.wav once before training starts

Saves baseline.wav + baseline.png in the checkpoint dir using the same
seed as the TI eval samples — direct A/B comparison at every checkpoint
without re-generating the baseline each time.

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

nodes/selva_textual_inversion_trainer.py

@@ -30,6 +30,7 @@ from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
 from selva_core.model.flow_matching import FlowMatching
 from .selva_lora_trainer import (
     _prepare_dataset,
+    _eval_sample,
     _spectral_metrics,
     _save_spectrogram,
     _smooth_losses,
@@ -300,6 +301,24 @@ class SelvaTextualInversionTrainer:
         ckpt_dir = out_path.parent / out_path.stem
         ckpt_dir.mkdir(parents=True, exist_ok=True)
 
+        # --- Baseline sample (once, before any training) ---
+        print(f"[TI Trainer] Generating baseline sample...", flush=True)
+        baseline_wav, baseline_sr = _eval_sample(
+            generator, feature_utils_orig, dataset, seq_cfg, device, dtype, seed=seed,
+        )
+        if baseline_wav is not None:
+            baseline_path = ckpt_dir / "baseline.wav"
+            try:
+                torchaudio.save(str(baseline_path), baseline_wav, baseline_sr)
+            except RuntimeError:
+                import soundfile as sf
+                sf.write(str(baseline_path), baseline_wav.squeeze(0).numpy(), baseline_sr)
+            try:
+                _save_spectrogram(baseline_wav, baseline_sr, ckpt_dir / "baseline.png")
+            except Exception:
+                pass
+            print(f"[TI Trainer] Baseline saved: {baseline_path}", flush=True)
+
         # --- Training loop ---
         generator.train()
         optimizer.zero_grad()