fix(ti-trainer): fix gradient flow and spectral metric shapes

- Replace in-place text_clip assignment with torch.cat so the computation
  graph correctly links text_input → learned_tokens; in-place assignment
  into a requires_grad=False leaf severs the graph and learned_tokens
  receives no gradients
- _spectral_metrics(wav, sr): was passing wav.unsqueeze(0) [1,1,L] instead
  of wav [1,L]; stft mean(dim=1) would return wrong shape [1,T] not [n_freqs]
- _save_spectrogram(wav, sr, ...): was passing wav.squeeze(0) [L] (1D)
  instead of wav [1,L] as the function expects

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

nodes/selva_textual_inversion_trainer.py

@@ -282,15 +282,19 @@ class SelvaTextualInversionTrainer:
             sync_f    = torch.stack([x.squeeze(0) for x in sync_list]).to(device, dtype)
             text_clip = torch.stack([x.squeeze(0) for x in text_list]).to(device, dtype).clone()
 
-            # Inject learned tokens into last n_tokens positions
-            text_clip[:, -n_tokens:, :] = learned_tokens.unsqueeze(0).expand(batch_size, -1, -1)
+            # Inject learned tokens into last n_tokens positions.
+            # Must use torch.cat (not in-place assignment) so the computation graph
+            # links text_input → learned_tokens and gradients flow correctly.
+            text_front = text_clip[:, :-n_tokens, :].detach()  # [B, 77-K, D], no grad
+            tokens_expanded = learned_tokens.unsqueeze(0).expand(batch_size, -1, -1)  # [B, K, D]
+            text_input = torch.cat([text_front, tokens_expanded], dim=1)  # [B, 77, D] with grad
 
             x1 = generator.normalize(x1)
             t  = torch.rand(batch_size, device=device, dtype=dtype)
             x0 = torch.randn_like(x1)
             xt = fm.get_conditional_flow(x0, x1, t)
 
-            v_pred = generator.forward(xt, clip_f, sync_f, text_clip, t)
+            v_pred = generator.forward(xt, clip_f, sync_f, text_input, t)
             loss   = fm.loss(v_pred, x0, x1).mean()
             loss.backward()
 
@@ -340,9 +344,9 @@ class SelvaTextualInversionTrainer:
                         import soundfile as sf
                         sf.write(str(wav_path), wav.squeeze(0).numpy(), sr)
 
-                    metrics = _spectral_metrics(wav.unsqueeze(0), sr)
+                    metrics = _spectral_metrics(wav, sr)
                     if metrics:
-                        img = _save_spectrogram(wav.squeeze(0), sr, ckpt_dir / f"step_{step:05d}.png")
+                        img = _save_spectrogram(wav, sr, ckpt_dir / f"step_{step:05d}.png")
                         print(f"[TI Trainer] step {step}  "
                               f"centroid={metrics['spectral_centroid_hz']:.0f}Hz  "
                               f"flatness={metrics['spectral_flatness']:.4f}  "