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/dsp_utils.py

@@ -1,6 +1,6 @@
 import numpy as np
-import librosa
 from pydub import AudioSegment
+from app.core.audio_features import load as _load
 
 def find_zero_crossing(y: np.ndarray, sr: int, target_time: float, window_seconds: float = 0.04) -> float:
     """
@@ -57,7 +57,7 @@ def find_nearest_zero_crossing_file(file_path: str, target_time_sec: float, sear
     """
     try:
         # Load mono audio for zero crossing analysis
-        y, sr = librosa.load(file_path, sr=None, mono=True)
+        y, sr = _load(file_path, sr=None, mono=True)
         return find_zero_crossing(y, sr, target_time_sec, search_window_sec)
     except Exception as e:
         print(f"Error finding zero crossing: {e}")
@@ -136,7 +136,7 @@ def generate_peak_waveform(file_path: str, num_peaks: int = 800) -> dict:
         dict: {"peaks": [...], "duration": float, "sample_rate": int}
     """
     # Load mono audio
-    y, sr = librosa.load(file_path, sr=None, mono=True)
+    y, sr = _load(file_path, sr=None, mono=True)
     
     total_samples = len(y)
     duration = float(total_samples) / sr
@@ -181,7 +181,7 @@ def generate_rms_waveform(file_path: str, num_points: int = 800) -> dict:
     Returns:
         dict: {"rms": [...], "duration": float, "sample_rate": int}
     """
-    y, sr = librosa.load(file_path, sr=None, mono=True)
+    y, sr = _load(file_path, sr=None, mono=True)
     
     total_samples = len(y)
     duration = float(total_samples) / sr