skip bare librosa sidecar, let preprocessing faf analysis handle caption fallback

app.py

@@ -673,7 +673,7 @@ def gradio_main():
                 )
                 if not caption_data:
                     use_understand = False
-                    _log(f"  {audio_fname}: GGUF LM too slow, switching to librosa for remaining files")
+                    _log(f"  {audio_fname}: GGUF LM too slow, skipping (preprocessing will analyze)")
             if caption_data:
                 bpm_s = caption_data.get("bpm", "?")
                 key_s = caption_data.get("keyscale", caption_data.get("key", "?"))
@@ -681,16 +681,7 @@ def gradio_main():
                 with open(sidecar_json, "w") as cj:
                     json.dump(caption_data, cj)
             else:
-                try:
-                    y_cap, sr_cap = _lr.load(full_path, sr=None, mono=True)
-                    tempo_arr, _ = _lr.beat.beat_track(y=y_cap, sr=sr_cap)
-                    bpm_val = int(round(float(tempo_arr.item() if hasattr(tempo_arr, 'item') else tempo_arr)))
-                    fallback = {"caption": "", "bpm": bpm_val, "key": "", "signature": "", "lyrics": ""}
-                    with open(sidecar_json, "w") as cj:
-                        json.dump(fallback, cj)
-                    _log(f"  {audio_fname}: librosa BPM={bpm_val}")
-                except Exception as cap_exc:
-                    _log(f"  {audio_fname}: caption failed: {cap_exc}")
+                _log(f"  {audio_fname}: will be analyzed in preprocessing (BPM + key + caption)")
             yield _log_text(), gr.Button(visible=False), gr.Button(visible=True), gr.File()
 
         if _training_cancel.is_set():