perf: slim daw_engine bundle 409MB->170MB + fix engine khong chay tren Windows

- Thay librosa bang app/core/audio_features.py (numpy/scipy/soundfile):
  load, beat_track, frames_to_time, spectral_centroid, rms, zero_crossing_rate,
  time_stretch, pitch_shift, chroma_stft. A/B ngang librosa (BPM <1% sai lech,
  pitch_shift chuan toi Hz). Loai bo llvmlite 171MB + scikit-learn + numba.
- Task layer 2 che do (app/tasks/worker.py): server giu celery; desktop slim
  chay in-process thread + registry, giu nguyen API contract (.delay/.id/status
  /tasks/{id}) nen frontend khong doi.
- engine.spec: excludes librosa/numba/llvmlite/sklearn/celery/redis/kombu/
  billiard/amqp/msgpack/yaml/PIL/cairosvg/zstandard/...; scan scipy gioi han
  scipy.signal; giu click (uvicorn.main import click).
- render_engine: scipy.signal thanh lazy import (giam cold start).
- tauri.conf.json: targets [nsis, msi] - NSIS tro lai (bundle nho) de
  hooks.nsh cai VC++ Redistributable - sua bug daw_engine.exe khong chay
  tren Windows (truoc day MSI-only khong chay hooks).
- build_linux.sh / build_macos.sh: build 1 lenh moi OS.
- Doc: DESKTOP_INSTALL_PLAN.md muc 5.1.
- Verify: 86 tests pass, engine dong goi upload/analyze/waveform/export OK.

app/core/ai_dsp_engine.py

@@ -75,13 +75,16 @@ class AIDSPEngine:
         t_end = min(total_duration, 4.0)
 
         try:
-            import librosa
-            # 1. Compute harmonic structural properties via Chroma Constant-Q Transform
-            chroma = librosa.feature.chroma_cqt(y=y_mono, sr=sr)
+            from app.core.audio_features import chroma_stft as _chroma_stft
+            # 1. Compute harmonic structural properties via Chroma (STFT-based,
+            #    thay chroma_cqt de lo bo librosa/numba/llvmlite ~171MB)
+            chroma = _chroma_stft(y=y_mono, sr=sr)
             
             # 2. Compile Self-Similarity Matrix (Cosine Recurrence Plot)
-            from sklearn.metrics.pairwise import cosine_similarity
-            ssm = cosine_similarity(chroma.T, chroma.T)
+            #    thay sklearn.metrics.pairwise.cosine_similarity bang numpy
+            c = chroma.T  # (n_frames, 12)
+            norms = np.linalg.norm(c, axis=1, keepdims=True)
+            ssm = (c @ c.T) / (norms @ norms.T + 1e-9)
             
             num_frames = ssm.shape[0]
             hop_length = 512