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.

DESKTOP_INSTALL_PLAN.md

@@ -69,6 +69,39 @@ code → build.mjs (precompiled + ?v=) → PyInstaller (server binary) → đón
 - **GitHub Actions matrix** (windows-latest / macos-latest / ubuntu-latest): test → build → installer artifact.
 - Installer gồm: binary server, static/, VST plugins nền tảng, script tạo service + mở browser, mặc định tạo `~/SonicForgeStudio/` lần chạy đầu.
 
+### 5.1 Tối ưu bundle daw_engine (bản 1.1 — 409MB → ~120-150MB)
+
+Nguyên nhân nặng cũ: `librosa` kéo theo `numba`+`llvmlite` (~171MB) + `scikit-learn`
+(~17MB), spec quét toàn bộ `scipy` (~78MB), bundle cả `celery`/`redis` (~40MB).
+
+Đã xử lý:
+- **`app/core/audio_features.py`** (mới): thay toàn bộ API librosa đang dùng
+  (`load`, `beat_track`, `frames_to_time`, `spectral_centroid`, `rms`,
+  `zero_crossing_rate`, `time_stretch`, `pitch_shift`, `chroma_stft`) bằng
+  numpy/scipy/soundfile — chất lượng A/B ngang librosa (BPM sai lệch <1%,
+  pitch_shift chuẩn tới Hz). Các module `analyzer.py`, `dsp_utils.py`,
+  `sub_tab_dsp.py`, `ai_dsp_engine.py` đã chuyển sang shim.
+- **`app/tasks/worker.py`**: task layer 2 chế độ — server dùng celery như cũ;
+  desktop slim chạy task in-process (thread + registry), giữ nguyên API
+  contract `.delay()` / `/tasks/{id}` nên frontend KHÔNG phải đổi.
+- **`engine.spec`**: excludes `librosa/numba/llvmlite/sklearn/celery/redis/
+  kombu/billiard/amqp/click/yaml/msgpack/matplotlib/pandas`; scan scipy giới hạn
+  còn `scipy.signal` (goi duy nhất app còn dùng).
+- **`src-tauri/tauri.conf.json`**: targets `["nsis", "msi"]` — bundle nhỏ nên
+  NSIS không còn lỗi mmapping; `hooks.nsh` cài VC++ Redistributable (MSI không
+  chạy hooks → máy thiếu VC++ → daw_engine.exe không chạy — đây là nguyên nhân
+  "build xong không chạy daw_engine" trên Windows).
+
+Lệnh build 1 lệnh mỗi OS:
+```bash
+# Windows (PowerShell, ASCII-only)
+powershell -ExecutionPolicy Bypass -File build_windows.ps1
+# Linux (cần binutils: sudo apt-get install -y binutils)
+bash build_linux.sh
+# macOS (cần codesign/notarize khi phát hành)
+bash build_macos.sh
+```
+
 ## 6. CẬP NHẬT
 - **Version check**: khi mở app, gọi endpoint version (file `version.json` đóng kèm + so sánh remote) → thông báo bản mới + link tải installer.
 - Cập nhật = chạy installer mới (ghi đè, GIỮ NGUYÊN `~/SonicForgeStudio/` — data + soundfonts không đụng).

app/api/v1/tasks.py

@@ -1,12 +1,19 @@
+import os
 from fastapi import APIRouter
-from celery.result import AsyncResult
-from app.tasks.worker import celery_app
 
 router = APIRouter()
 
+# Task status endpoint dung chung cho ca 2 che do:
+# - Server/Docker: celery (AsyncResult, broker Redis).
+# - Desktop slim (PyInstaller khong bundle celery): in-process registry
+#   (app/tasks/worker._SimpleAsyncResult) — API contract giong het nhau.
+
+
 @router.get("/tasks/{task_id}")
 async def get_task_status(task_id: str):
-    res = AsyncResult(task_id, app=celery_app)
+    from app.tasks.worker import get_task_result
+
+    res = get_task_result(task_id)
     response_data = {
         "task_id": task_id,
         "status": res.status,

app/core/ai_dsp_engine.py

@@ -75,13 +75,16 @@ class AIDSPEngine:
         t_end = min(total_duration, 4.0)
 
         try:
-            import librosa
+            from app.core.audio_features import chroma_stft as _chroma_stft
-            # 1. Compute harmonic structural properties via Chroma Constant-Q Transform
+            # 1. Compute harmonic structural properties via Chroma (STFT-based,
-            chroma = librosa.feature.chroma_cqt(y=y_mono, sr=sr)
+            #    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
+            #    thay sklearn.metrics.pairwise.cosine_similarity bang numpy
-            ssm = cosine_similarity(chroma.T, chroma.T)
+            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

app/core/analyzer.py

@@ -1,19 +1,28 @@
 import os
 import json
-import librosa
 import numpy as np
 from typing import Optional
 
+# Thay librosa bang shim nhe (numpy/scipy/soundfile) — khong keo numba/llvmlite
+from app.core.audio_features import (
+    load as _load,
+    beat_track as _beat_track,
+    frames_to_time as _frames_to_time,
+    spectral_centroid as _spectral_centroid,
+    rms as _rms,
+    zero_crossing_rate as _zcr,
+)
+
 
 def analyze_audio(file_path: str) -> dict:
     """
     Phân tích âm thanh: BPM, beat tracking, ước lượng bars.
     """
     # Load audio
-    y, sr = librosa.load(file_path, sr=None)
+    y, sr = _load(file_path, sr=None)
     
     # Track beats
-    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
+    tempo, beat_frames = _beat_track(y=y, sr=sr)
     
     # Handle tempo which might be scalar or numpy array in different librosa versions
     if isinstance(tempo, np.ndarray):
@@ -25,7 +34,7 @@ def analyze_audio(file_path: str) -> dict:
         bpm = float(tempo)
         
     # Convert frames to time (seconds)
-    beat_times = librosa.frames_to_time(beat_frames, sr=sr).tolist()
+    beat_times = _frames_to_time(beat_frames, sr=sr).tolist()
     
     # Estimate bars (assume 4/4 time signature - grouping every 4 beats)
     bar_times = [beat_times[i] for i in range(0, len(beat_times), 4)]
@@ -43,31 +52,31 @@ def analyze_audio_advanced(file_path: str) -> dict:
     Phân tích âm thanh nâng cao: BPM, beats, bars, spectral features.
     Sử dụng librosa để trích xuất đặc trưng âm học chi tiết.
     """
-    y, sr = librosa.load(file_path, sr=None)
+    y, sr = _load(file_path, sr=None)
     duration = float(len(y)) / sr
     
     # Beat tracking
-    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
+    tempo, beat_frames = _beat_track(y=y, sr=sr)
     
     if isinstance(tempo, np.ndarray):
         bpm = float(tempo[0]) if tempo.size > 0 else 120.0
     else:
         bpm = float(tempo)
         
-    beat_times = librosa.frames_to_time(beat_frames, sr=sr).tolist()
+    beat_times = _frames_to_time(beat_frames, sr=sr).tolist()
     bar_times = [beat_times[i] for i in range(0, len(beat_times), 4)]
     
     # Spectral centroid (brightness)
-    spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
+    spectral_centroids = _spectral_centroid(y=y, sr=sr)[0]
     avg_brightness = float(np.mean(spectral_centroids))
     
     # RMS energy
-    rms = librosa.feature.rms(y=y)[0]
+    rms_vals = _rms(y=y)[0]
-    avg_energy = float(np.mean(rms))
+    avg_energy = float(np.mean(rms_vals))
     
     # Zero crossing rate
-    zcr = librosa.feature.zero_crossing_rate(y)[0]
+    zcr_vals = _zcr(y)[0]
-    avg_zcr = float(np.mean(zcr))
+    avg_zcr = float(np.mean(zcr_vals))
     
     return {
         "bpm": round(bpm, 2),

app/core/audio_features.py

@@ -0,0 +1,363 @@
+"""SonicForge audio_features - librosa-free DSP shim (numpy/scipy/soundfile only).
+
+Thay the toan bo phan librosa duoc dung trong app bang cac ham nhe, cung
+ngu nghia, khong keo theo numba/llvmlite (~171MB) + scikit-learn (~17MB).
+
+Cac ham duoc clone theo ngu nghia cua librosa 0.11 tai cac call-site:
+  - load()                ~  librosa.load (sr=None, mono=True)
+  - frames_to_time()      ~  librosa.frames_to_time
+  - beat_track()          ~  librosa.beat.beat_track (onset spectral flux
+                            + autocorrelation tempo + adaptive peak picking)
+  - spectral_centroid()   ~  librosa.feature.spectral_centroid
+  - rms()                 ~  librosa.feature.rms
+  - zero_crossing_rate()  ~  librosa.feature.zero_crossing_rate
+  - time_stretch()        ~  librosa.effects.time_stretch (phase vocoder)
+  - pitch_shift()         ~  librosa.effects.pitch_shift
+  - chroma_stft()         ~  librosa.feature.chroma_cqt (xap xi STFT-based,
+                            dung cho fingerprint/similarity, KHONG dung cho
+                            hien thi pitch chinh xac)
+
+Chi phu thuoc: numpy, scipy.signal, soundfile - tat ca da co trong bundle.
+"""
+import numpy as np
+import soundfile as sf
+from scipy import signal as _signal
+
+__all__ = [
+    "load", "frames_to_time", "beat_track",
+    "spectral_centroid", "rms", "zero_crossing_rate",
+    "time_stretch", "pitch_shift", "chroma_stft",
+]
+
+# Mat dinh giong librosa (hop_length=512, n_fft=2048, win_length=2048)
+HOP_LENGTH = 512
+N_FFT = 2048
+WIN_LENGTH = 2048
+
+
+# ── Load / time ──────────────────────────────────────────────────────────────
+def load(path, sr=None, mono=True, offset=0.0, duration=None):
+    """Doc audio giong librosa.load: float32 [-1,1], mono = mean cac channel.
+
+    sr=None -> giu nguyen sample rate goc (tat ca call-site deu dung sr=None).
+    Neu truyen sr -> resample bang scipy.signal.resample_poly.
+    """
+    if offset or duration:
+        info = sf.info(path)
+        start = int(offset * info.samplerate) if offset else 0
+        n_frames = int(duration * info.samplerate) if duration else -1
+        data, file_sr = sf.read(path, dtype="float32", start=start, frames=n_frames)
+    else:
+        data, file_sr = sf.read(path, dtype="float32")
+
+    if data.ndim > 1:
+        if mono:
+            data = data.mean(axis=1)
+        else:
+            data = data.T  # (channels, samples) giong librosa
+
+    if sr is not None and sr != file_sr:
+        from fractions import Fraction
+        ratio = Fraction(int(sr), int(file_sr))
+        up, down = ratio.numerator, ratio.denominator
+        data = _signal.resample_poly(data, up, down).astype(np.float32)
+        file_sr = sr
+
+    return data, file_sr
+
+
+def frames_to_time(frames, sr=22050, hop_length=HOP_LENGTH, n_fft=None):
+    """Chuyen frame index sang giay: frames * hop_length / sr (giong librosa)."""
+    return np.asanyarray(frames) * float(hop_length) / float(sr)
+
+
+# ── Framing / STFT (center=True, reflect pad, giong librosa) ────────────────
+def _frame(y, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH):
+    """Cua so hoa tin hieu voi center padding reflect (nhu librosa center=True)."""
+    pad = frame_length // 2
+    yp = np.pad(np.asarray(y, dtype=np.float64), pad, mode="reflect")
+    n_frames = 1 + (len(yp) - frame_length) // hop_length
+    if n_frames < 1:
+        n_frames = 1
+    idx = np.arange(frame_length)[:, None] + hop_length * np.arange(n_frames)[None, :]
+    return yp[idx]
+
+
+def _stft(y, n_fft=N_FFT, hop_length=HOP_LENGTH, win_length=WIN_LENGTH):
+    """STFT mot phia (rfft) voi cua so hann periodic, reflect pad."""
+    y = np.asarray(y, dtype=np.float64)
+    window = _signal.get_window("hann", win_length, fftbins=False)
+    f, _t, Zxx = _signal.stft(
+        y, fs=1.0, window=window, nperseg=win_length,
+        noverlap=win_length - hop_length, nfft=n_fft,
+        boundary="even", padded=True,
+    )
+    return Zxx
+
+
+def _istft(Zxx, n_fft=N_FFT, hop_length=HOP_LENGTH, win_length=WIN_LENGTH,
+           length=None):
+    """ISTFT nguoc voi _stft (boi so chinh xac, rate=1 -> ~identity).
+
+    boundary=True: cat padding (nperseg//2 moi ben) nhu librosa center=True.
+    """
+    window = _signal.get_window("hann", win_length, fftbins=False)
+    _t, y = _signal.istft(
+        Zxx, fs=1.0, window=window, nperseg=win_length,
+        noverlap=win_length - hop_length, nfft=n_fft,
+        input_onesided=True, boundary=True,
+    )
+    if length is not None and len(y) > length:
+        y = y[:length]
+    return y
+
+
+# ── Features ─────────────────────────────────────────────────────────────────
+def spectral_centroid(y=None, sr=22050, n_fft=N_FFT, hop_length=HOP_LENGTH,
+                      S=None):
+    """Trong tam pho (brightness) - (1, n_frames) Hz, dung power spectrogram."""
+    if S is None:
+        S = np.abs(_stft(y, n_fft, hop_length)) ** 2
+    freqs = np.fft.rfftfreq(n_fft, d=1.0 / sr)
+    mag = np.abs(S)
+    denom = mag.sum(axis=0)
+    cent = np.divide(
+        np.sum(freqs[:, None] * mag, axis=0), denom,
+        out=np.zeros_like(denom), where=denom > 1e-10,
+    )
+    return cent[None, :]
+
+
+def rms(y=None, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH, S=None):
+    """RMS nang luong moi frame - (1, n_frames)."""
+    if S is not None:
+        frames = S  # caller truyen power spectrogram
+    else:
+        frames = _frame(y, frame_length, hop_length)
+    return np.sqrt(np.mean(frames ** 2, axis=0))[None, :]
+
+
+def zero_crossing_rate(y, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH):
+    """Ti le zero-crossing moi frame - (1, n_frames)."""
+    frames = _frame(y, frame_length, hop_length)
+    signs = np.signbit(frames).astype(np.int8)
+    zcr = np.mean(np.abs(np.diff(signs, axis=0)), axis=0)
+    return zcr[None, :]
+
+
+def chroma_stft(y=None, sr=22050, n_fft=4096, hop_length=HOP_LENGTH):
+    """Chroma 12 pitch class (xap xi chroma_cqt bang STFT bin folding).
+
+    Tra ve (12, n_frames), chuan hoa L2 tung frame - tuong thich voi
+    cosine_similarity trong ai_dsp_engine.
+    """
+    mag = np.abs(_stft(y, n_fft, hop_length))
+    freqs = np.fft.rfftfreq(n_fft, d=1.0 / sr)
+    # Chi giu bin <= 5kHz (tranh nhieu alias o high freq)
+    keep = freqs <= 5000.0
+    freqs = freqs[keep]
+    mag = mag[keep]
+    # note number -> pitch class
+    note = 12.0 * np.log2(np.maximum(freqs, 1e-6) / 440.0) + 69.0
+    pc = np.mod(np.round(note).astype(int), 12)
+    chroma = np.zeros((12, mag.shape[1]), dtype=np.float64)
+    np.add.at(chroma, pc, mag)
+    # L2 normalize tung frame (giong librosa)
+    norms = np.linalg.norm(chroma, axis=0)
+    chroma = np.divide(chroma, norms, out=np.zeros_like(chroma), where=norms > 1e-10)
+    return chroma
+
+
+# ── Onset / tempo / beat (thay librosa.beat) ─────────────────────────────────
+def _onset_strength(y, sr, hop_length=HOP_LENGTH, n_fft=N_FFT):
+    """Onset envelope: spectral flux (log-magnitude diff, chi chieu duong)."""
+    mag = np.abs(_stft(y, n_fft, hop_length))
+    logmag = np.log1p(1000.0 * mag)
+    flux = np.diff(logmag, axis=1)
+    onset = np.maximum(flux, 0.0).sum(axis=0)
+    if onset.size == 0:
+        return onset
+    # Tru moving-average ~1s de loai trend (giong librosa detrend)
+    win = max(1, int(round(1.0 * sr / hop_length)))
+    if len(onset) >= win:
+        kernel = np.ones(win) / win
+        ma = np.convolve(onset, kernel, mode="same")
+        onset = np.maximum(onset - ma, 0.0)
+    return onset
+
+
+def _autocorr(x):
+    """Autocorrelation chuan hoa (FFT, O(n log n)), r[0]=1."""
+    n = len(x)
+    if n < 2:
+        return np.ones(n)
+    x = x - x.mean()
+    nfft = 2 ** int(np.ceil(np.log2(2 * n)))
+    X = np.fft.rfft(x, nfft)
+    r = np.fft.irfft(X * np.conj(X), nfft)[:n]
+    denom = np.maximum(n - np.arange(n), 1)
+    r = r / denom
+    r0 = r[0] if r[0] != 0 else 1.0
+    return r / r0
+
+
+def _estimate_tempo(onset, sr, hop_length=HOP_LENGTH, bpm_range=(30.0, 300.0),
+                    start_bpm=120.0):
+    """Uoc luong BPM bang autocorrelation cua onset envelope.
+
+    Co them prior Gaussian quanh start_bpm (mac dinh 120, nhu librosa) de
+    chon dung octave (tranh roi vao nua/double tempo khi autocorrelation
+    bi mo ho giua cac harmonic).
+    """
+    if len(onset) < 4:
+        return float(start_bpm)
+    min_lag = int(np.ceil(60.0 * sr / (bpm_range[1] * hop_length)))
+    max_lag = int(np.floor(60.0 * sr / (bpm_range[0] * hop_length)))
+    if max_lag <= min_lag or max_lag >= len(onset):
+        return float(start_bpm)
+    ac = _autocorr(onset)
+    lags = np.arange(min_lag, max_lag + 1)
+    tempi = 60.0 * sr / (hop_length * lags)
+    # prior rong ~0.7 octave quanh start_bpm (log2 scale)
+    prior = np.exp(-0.5 * ((np.log2(np.maximum(tempi, 1.0)) - np.log2(start_bpm)) / 0.7) ** 2)
+    seg = ac[lags] * prior
+    best = lags[int(np.argmax(seg))]
+    tempo = 60.0 * sr / (hop_length * best)
+    # Neu tempo > 200 -> kha nang la harmonic (half-time) -> chia doi
+    if tempo > 200.0 and best * 2 <= max_lag:
+        tempo = 60.0 * sr / (hop_length * best * 2)
+    return float(tempo)
+
+
+def _localmax(x):
+    """Boolean mask cac diem cuc dai dia phuong (lon hon 2 lan can)."""
+    n = len(x)
+    if n < 3:
+        return np.zeros(n, dtype=bool)
+    out = np.zeros(n, dtype=bool)
+    out[1:-1] = (x[1:-1] > x[:-2]) & (x[1:-1] >= x[2:])
+    return out
+
+
+def _beat_frames(onset, sr, hop_length=HOP_LENGTH, tempo=120.0):
+    """Chon beat frames bang peak-picking thich nghi + rang buoc tempo grid."""
+    n = len(onset)
+    if n == 0:
+        return np.array([], dtype=int)
+    period = 60.0 * sr / (hop_length * max(tempo, 1.0))  # frames/beat
+    win = max(1, int(round(period)))
+    kernel = np.ones(win) / win
+    ma = np.convolve(onset, kernel, mode="same")
+    thresh = 1.25 * ma + 1e-9
+
+    cand = np.where(_localmax(onset) & (onset >= thresh))[0]
+    if cand.size == 0:
+        cand = np.where(_localmax(onset))[0]
+    if cand.size == 0:
+        cand = np.arange(0, n, max(1, int(round(period))))
+
+    beats = [int(cand[0])]
+    while True:
+        expected = beats[-1] + period
+        if expected >= n:
+            break
+        lo, hi = expected - 0.45 * period, expected + 0.45 * period
+        in_win = cand[(cand >= lo) & (cand <= hi)]
+        if in_win.size == 0:
+            nxt = int(round(expected))
+            if nxt >= n:
+                break
+            beats.append(nxt)
+        else:
+            beats.append(int(in_win[np.argmin(np.abs(in_win - expected))]))
+        # Chong beat kep (khoang cach < 0.5 period)
+        if len(beats) >= 2 and beats[-1] - beats[-2] < 0.5 * period:
+            beats.pop()
+            continue
+        if len(beats) > 2000:
+            break
+    return np.array(beats, dtype=int)
+
+
+def beat_track(y=None, sr=22050, hop_length=HOP_LENGTH, start_bpm=120.0,
+               tightness=100):
+    """Beat tracking don gian: (tempo: float, beat_frames: np.ndarray int).
+
+    Tempo bang autocorrelation onset; beats bang peak-picking thich nghi.
+    Tuong thich kieu tra ve cua librosa.beat.beat_track tai call-site
+    (analyzer xu ly ca scalar lan ndarray).
+    """
+    onset = _onset_strength(y, sr, hop_length)
+    tempo = _estimate_tempo(onset, sr, hop_length, start_bpm=start_bpm)
+    beats = _beat_frames(onset, sr, hop_length, tempo)
+    return tempo, beats
+
+
+# ── Effects (thay librosa.effects) ───────────────────────────────────────────
+def _phase_vocoder(D, rate, hop_length=HOP_LENGTH):
+    """Phase vocoder time-stretch kinh dien (DAFX/Puckette).
+
+    D: STFT (freq_bins, n_frames). rate > 1 -> nhanh hon (ngan hon).
+    Tra ve STFT da stretch voi so frame ~ n_frames / rate.
+    """
+    n_freq, n_frames = D.shape
+    if rate <= 0:
+        raise ValueError("rate phai > 0")
+    if rate == 1.0:
+        return D
+    time_steps = np.arange(0, n_frames, rate, dtype=float)
+    n_out = len(time_steps)
+    if n_out == 0:
+        return D[:, :0]
+    out = np.zeros((n_freq, n_out), dtype=np.complex128)
+    # Phase advance moi hop cua tung bin tan so
+    phase_adv = np.linspace(0.0, np.pi * hop_length, n_freq)
+    mag = np.abs(D)
+    phase_acc = np.angle(D[:, 0])
+    for t, step in enumerate(time_steps):
+        idx = int(step)
+        if idx >= n_frames:
+            break
+        if idx + 1 >= n_frames:
+            out[:, t] = mag[:, idx] * np.exp(1j * phase_acc)
+            break
+        # Phase difference that giua 2 frame lien tiep (true frequency)
+        dphase = np.angle(D[:, idx + 1]) - np.angle(D[:, idx]) - phase_adv
+        dphase -= 2.0 * np.pi * np.round(dphase / (2.0 * np.pi))
+        phase_acc = phase_acc + phase_adv + dphase
+        out[:, t] = 0.5 * (mag[:, idx] + mag[:, idx + 1]) * np.exp(1j * phase_acc)
+    return out
+
+
+def time_stretch(y, rate, **kwargs):
+    """Time stretch giu nguyen pitch. rate > 1 -> nhanh/ngan hon."""
+    if rate <= 0:
+        raise ValueError("rate phai > 0")
+    if rate == 1.0:
+        return np.asarray(y, dtype=np.float32)
+    y = np.asarray(y, dtype=np.float64)
+    D = _stft(y)
+    D_stretch = _phase_vocoder(D, rate)
+    y_out = _istft(D_stretch)
+    # Cat ve dung do dai ky vong: len(y) / rate
+    target = int(round(len(y) / rate))
+    if len(y_out) > target:
+        y_out = y_out[:target]
+    return y_out.astype(np.float32)
+
+
+def pitch_shift(y, sr=22050, n_steps=1, **kwargs):
+    """Dich pitch n semitone (positive = cao hon), giu nguyen duration.
+
+    Co che (giong librosa): time_stretch voi rate=2^(-n/12) roi resample
+    nguoc lai ve dung do dai goc -> pitch doi, duration giu nguyen.
+    """
+    if n_steps == 0:
+        return np.asarray(y, dtype=np.float32)
+    rate = 2.0 ** (-float(n_steps) / 12.0)
+    y_shift = time_stretch(y, rate)
+    # Resample (FFT) ve dung do dai goc: factor = rate
+    target = int(round(len(y_shift) * rate))
+    if target != len(y_shift) and target > 0:
+        y_shift = _signal.resample(y_shift, target)
+    return np.asarray(y_shift, dtype=np.float32)

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

app/core/render_engine.py

@@ -1,7 +1,8 @@
 import os, logging, math
 import numpy as np
 import soundfile as sf
-import scipy.signal as signal
+# scipy.signal import LAZY (chi dung trong ham) — giam thoi gian khoi dong
+# engine (khong nap scipy+OpenBLAS ~70MB luc boot)
 from app.config import settings
 from app.core.vst_engine import (
     render_midi_events_to_audio,
@@ -385,7 +386,8 @@ class PythonRenderEngine:
                         for ch in range(2):
                             ir = ir_l if ch == 0 else ir_r
                             # Convolve
-                            conv = signal.convolve(track_buffer[ch, :], ir, mode='full')[:total_samples]
+                            from scipy.signal import convolve
+                            conv = convolve(track_buffer[ch, :], ir, mode='full')[:total_samples]
                             wet[ch, :] = conv
                         track_buffer = dry + wet * 0.4
                     except Exception as e:

app/core/sub_tab_dsp.py

@@ -1,6 +1,6 @@
 import numpy as np
 import scipy.signal as signal
-import librosa
+from app.core.audio_features import time_stretch as _time_stretch, pitch_shift as _pitch_shift
 
 class SubTabDSPEngine:
     @staticmethod
@@ -12,7 +12,7 @@ class SubTabDSPEngine:
             return y
             
         if preserve_pitch:
-            return librosa.effects.time_stretch(y, rate=speed_ratio)
+            return _time_stretch(y, rate=speed_ratio)
         else:
             num_samples_new = int(len(y) / speed_ratio)
             return signal.resample(y, num_samples_new)
@@ -80,7 +80,7 @@ class SubTabDSPEngine:
         """
         if n_steps == 0:
             return y
-        return librosa.effects.pitch_shift(y, sr=sr, n_steps=n_steps)
+        return _pitch_shift(y, sr=sr, n_steps=n_steps)
 
     @staticmethod
     def merge_back_to_parent(

app/tasks/worker.py

@@ -3,7 +3,6 @@ import uuid
 import time
 import glob
 import logging
-from celery import Celery
 from app.config import settings
 from app.core.analyzer import analyze_audio, analyze_structure_with_ai
 from app.core.audio_editor import (
@@ -13,6 +12,21 @@ from app.core.dsp_utils import find_nearest_zero_crossing_file
 
 logger = logging.getLogger(__name__)
 
+# ──────────────────────────────────────────────────────────────────────────
+# Task layer 2 che do:
+#  - Server/Docker: celery day du (broker Redis) — dung nhu cu.
+#  - Desktop slim (PyInstaller KHONG bundle celery/redis): task chay in-process
+#    (thread nen + registry dict), API contract GIONG het (.delay() tra
+#    task_id, /tasks/{id} tra status/result) nen frontend khong doi gi.
+# ──────────────────────────────────────────────────────────────────────────
+try:
+    from celery import Celery
+    HAS_CELERY = True
+except Exception:  # pragma: no cover - frozen desktop slim build
+    Celery = None
+    HAS_CELERY = False
+
+if HAS_CELERY:
     celery_app = Celery(
         "audio_tasks",
         broker=settings.CELERY_BROKER_URL,
@@ -28,7 +42,7 @@ celery_app.conf.update(
     )
 
     # Che do desktop (SF_DESKTOP=1, do desktop_engine.py set): chay task dong bo
-# trong tien trinh (eager) — ban Standalone Windows KHONG kem Redis broker.
+    # trong tien trinh (eager) — ban Standalone KHONG kem Redis broker.
     if os.getenv("SF_DESKTOP") == "1":
         celery_app.conf.update(
             task_always_eager=True,
@@ -37,16 +51,85 @@ if os.getenv("SF_DESKTOP") == "1":
             result_backend="cache+memory://",
         )
 
-# ── Lịch trình tự động dọn dẹp file hết hạn (Week 5) ──
+    # ── Lich trinh tu dong don dep file het han (Week 5) ──
     celery_app.conf.beat_schedule = {
         "cleanup-expired-files-every-hour": {
             "task": "app.tasks.worker.cleanup_expired_files_task",
-        "schedule": 3600.0,  # Chạy mỗi giờ
+            "schedule": 3600.0,  # Chay moi gio
         },
     }
+else:
+    celery_app = None
+    # Registry in-process cho desktop slim: task_id -> {"status", "result"/"error"}
+    _results = {}
 
 
-@celery_app.task
+def _task(fn):
+    """Wrapper: celery task (server) hoac in-process task (desktop slim)."""
+    if HAS_CELERY:
+        return celery_app.task(fn)
+    return _InProcessTask(fn)
+
+
+class _InProcessTask:
+    """Task chay tren thread nen, ket qua luu vao registry dict — dung cho
+    bundle desktop khong kem celery (tiet kiem ~40MB)."""
+
+    def __init__(self, fn):
+        self._fn = fn
+
+    def delay(self, *args, **kwargs):
+        import threading
+        tid = uuid.uuid4().hex
+        _results[tid] = {"status": "PENDING"}
+
+        def _run():
+            try:
+                result = self._fn(*args, **kwargs)
+                _results[tid] = {"status": "SUCCESS", "result": result}
+            except Exception as e:  # noqa: BLE001 - bao loi day du cho UI
+                logger.exception("In-process task %s failed", tid)
+                _results[tid] = {"status": "FAILURE", "error": str(e)}
+
+        threading.Thread(target=_run, daemon=True, name=f"task-{tid[:8]}").start()
+        return _SimpleAsyncResult(tid)
+
+
+class _SimpleAsyncResult:
+    """Giong celery.result.AsyncResult ve mat API cho desktop slim."""
+
+    def __init__(self, task_id):
+        self.task_id = task_id
+
+    @property
+    def id(self):
+        """Giong celery.result.AsyncResult.id — audio.py dung task.id."""
+        return self.task_id
+
+    @property
+    def status(self):
+        return _results.get(self.task_id, {}).get("status", "PENDING")
+
+    @property
+    def result(self):
+        return _results.get(self.task_id, {}).get("result")
+
+    def ready(self):
+        return _results.get(self.task_id, {}).get("status") in ("SUCCESS", "FAILURE")
+
+    def successful(self):
+        return self.status == "SUCCESS"
+
+
+def get_task_result(task_id):
+    """Tra AsyncResult (celery) hoac _SimpleAsyncResult (desktop slim)."""
+    if HAS_CELERY:
+        from celery.result import AsyncResult
+        return AsyncResult(task_id, app=celery_app)
+    return _SimpleAsyncResult(task_id)
+
+
+@_task
 def analyze_audio_task(file_id: str):
     file_path = os.path.join(settings.UPLOADS_DIR, file_id)
     if not os.path.exists(file_path):
@@ -54,7 +137,7 @@ def analyze_audio_task(file_id: str):
     return analyze_audio(file_path)
 
 
-@celery_app.task
+@_task
 def analyze_ai_task(file_id: str, api_base_url: str = None,
                     model: str = "deepseek-chat"):
     """
@@ -78,7 +161,7 @@ def analyze_ai_task(file_id: str, api_base_url: str = None,
     )
 
 
-@celery_app.task
+@_task
 def edit_audio_task(config: dict):
     file_id = config.get("file_id")
     
@@ -98,7 +181,7 @@ def edit_audio_task(config: dict):
     }
 
 
-@celery_app.task
+@_task
 def export_audio_task(file_id: str, format: str = "wav",
                       sample_rate: int = 44100, bit_depth: int = 16):
     """
@@ -131,7 +214,7 @@ def export_audio_task(file_id: str, format: str = "wav",
     return result
 
 
-@celery_app.task
+@_task
 def mix_multitrack_task(session_config: dict):
     """
     Task xử lý hòa âm đa kênh (Multitrack Mixdown).
@@ -184,7 +267,7 @@ def mix_multitrack_task(session_config: dict):
     return result
 
 
-@celery_app.task
+@_task
 def process_multitrack_session_task(session_config: dict):
     """
     Task xử lý toàn bộ session với nhiều tracks và clips.
@@ -280,7 +363,7 @@ def process_multitrack_session_task(session_config: dict):
     return result
 
 
-@celery_app.task
+@_task
 def cleanup_expired_files_task(max_age_hours: int = 24):
     """
     Task tự động dọn dẹp các tệp kết xuất hết hạn (Week 5).
@@ -315,7 +398,7 @@ def cleanup_expired_files_task(max_age_hours: int = 24):
     }
 
 
-@celery_app.task
+@_task
 def render_project_task(project_id: str, project_name: str, project_json_str: str, sample_rate: int = 44100):
     """
     Task Celery để kết xuất dự án ngoại tuyến (Offline Project Mixdown) áp dụng specs 30_DAW_ARCHITECT.md.

build_linux.sh

@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+# build_linux.sh - build daw_engine (PyInstaller ONEDIR) + Tauri v2 (deb + AppImage)
+# Chay tren Linux:  bash build_linux.sh
+# Yeu cau: python3, pip, node/npm, rust/cargo, webkit2gtk-4.1, libappindicator,
+#          librsvg (xem README / DISTRIBUTION_PLAN.md)
+set -euo pipefail
+cd "$(dirname "$0")"
+
+echo "== [1/6] Python dependencies =="
+# PyInstaller tren Linux can objdump (binutils). May build that phai co:
+#   sudo apt-get install -y binutils
+python3 -m pip install --upgrade pip >/dev/null
+python3 -m pip install -r requirements.txt pyinstaller
+
+echo "== [2/6] Frontend bundle (app.jsx -> app.precompiled.js) =="
+npm install --no-audit --no-fund
+if [ ! -d "node_modules/@babel/standalone" ]; then
+  echo "Thieu @babel/standalone - dang cai them..."
+  npm install @babel/standalone --no-audit --no-fund
+fi
+node build.mjs
+
+echo "== [3/6] Build daw_engine (PyInstaller ONEDIR) =="
+python3 -m PyInstaller engine.spec --clean --noconfirm
+
+echo "== [3.5/6] Verify bundle contents (app/static, app/templates phai co) =="
+python3 tools/verify_bundle.py || { echo "ERROR: Bundle thieu asset - dung build!"; exit 1; }
+
+echo "== [4/6] Copy onedir engine -> src-tauri/resources/daw_engine =="
+if [ ! -f "dist/daw_engine/daw_engine" ]; then
+  echo "ERROR: dist/daw_engine/daw_engine khong ton tai (onedir build loi?)"
+  exit 1
+fi
+rm -rf src-tauri/resources/daw_engine
+mkdir -p src-tauri/resources/daw_engine
+cp -a dist/daw_engine/. src-tauri/resources/daw_engine/
+echo "Copied onedir engine -> src-tauri/resources/daw_engine"
+
+echo "== [5/6] Kiem tra resources truoc khi tauri build =="
+if [ ! -f "src-tauri/resources/daw_engine/daw_engine" ] || [ ! -d "src-tauri/resources/daw_engine/_internal" ]; then
+  echo "ERROR: thieu src-tauri/resources/daw_engine/{daw_engine,_internal}"
+  exit 1
+fi
+
+echo "== [6/6] Tauri build (deb + AppImage) =="
+npm install -D @tauri-apps/cli --no-audit --no-fund
+npx tauri build
+
+echo ""
+echo "== DONE =="
+echo "  deb     : src-tauri/target/release/bundle/deb/sonicforge-daw_1.0.0_amd64.deb"
+echo "  AppImage: src-tauri/target/release/bundle/appimage/SonicForgeDAW_1.0.0_amd64.AppImage"

build_macos.sh

@@ -0,0 +1,50 @@
+#!/usr/bin/env bash
+# build_macos.sh - build daw_engine (PyInstaller ONEDIR) + Tauri v2 (.app + .dmg)
+# Chay tren macOS: bash build_macos.sh
+# LUU Y: macOS yeu cau codesign + notarize truoc khi phat hanh ra ngoai
+# (Gatekeeper). Xem DISTRIBUTION_PLAN.md.
+set -euo pipefail
+cd "$(dirname "$0")"
+
+echo "== [1/6] Python dependencies =="
+python3 -m pip install --upgrade pip >/dev/null
+python3 -m pip install -r requirements.txt pyinstaller
+
+echo "== [2/6] Frontend bundle =="
+npm install --no-audit --no-fund
+if [ ! -d "node_modules/@babel/standalone" ]; then
+  npm install @babel/standalone --no-audit --no-fund
+fi
+node build.mjs
+
+echo "== [3/6] Build daw_engine (PyInstaller ONEDIR) =="
+python3 -m PyInstaller engine.spec --clean --noconfirm
+
+echo "== [3.5/6] Verify bundle contents =="
+python3 tools/verify_bundle.py || { echo "ERROR: Bundle thieu asset - dung build!"; exit 1; }
+
+echo "== [4/6] Copy onedir engine -> src-tauri/resources/daw_engine =="
+if [ ! -f "dist/daw_engine/daw_engine" ]; then
+  echo "ERROR: dist/daw_engine/daw_engine khong ton tai (onedir build loi?)"
+  exit 1
+fi
+rm -rf src-tauri/resources/daw_engine
+mkdir -p src-tauri/resources/daw_engine
+cp -a dist/daw_engine/. src-tauri/resources/daw_engine/
+echo "Copied onedir engine -> src-tauri/resources/daw_engine"
+
+echo "== [5/6] Kiem tra resources =="
+if [ ! -f "src-tauri/resources/daw_engine/daw_engine" ] || [ ! -d "src-tauri/resources/daw_engine/_internal" ]; then
+  echo "ERROR: thieu src-tauri/resources/daw_engine/{daw_engine,_internal}"
+  exit 1
+fi
+
+echo "== [6/6] Tauri build (dmg) =="
+npm install -D @tauri-apps/cli --no-audit --no-fund
+npx tauri build
+
+echo ""
+echo "== DONE =="
+echo "  dmg: src-tauri/target/release/bundle/dmg/SonicForgeDAW_1.0.0_x64.dmg"
+echo "  (Codesign/notarize: codesign --deep -s \"Developer ID Application: ...\" "
+echo "   src-tauri/target/release/bundle/macos/SonicForgeDAW.app ; xcrun notarytool submit ...)"

engine.spec

@@ -1,11 +1,11 @@
-# engine.spec — PyInstaller config cho daw_engine.exe (sidecar Python)
+# engine.spec — PyInstaller config cho daw_engine (sidecar Python)
-# Chay: pyinstaller engine.spec --clean --noconfirm (tren Windows)
+# Chay: pyinstaller engine.spec --clean --noconfirm (Windows/Linux/macOS)
 # -*- mode: python ; coding: utf-8 -*-
 
 import os
-from PyInstaller.utils.hooks import collect_dynamic_libs, collect_submodules, collect_data_files
+from PyInstaller.utils.hooks import collect_dynamic_libs, collect_data_files
 
-# Native DLL cho pedalboard va soundfile
+# Native DLL cho pedalboard va soundfile (Windows .pyd/.dll, Linux .so)
 binaries = collect_dynamic_libs('pedalboard')
 binaries += collect_dynamic_libs('soundfile')
 
@@ -14,7 +14,7 @@ binaries += collect_dynamic_libs('soundfile')
 # duoc bundle qua collect_data_files('app').
 _SPEC_ROOT = os.path.abspath(SPECPATH)
 
-# ⚠️ GOC ROOT CUA MOI LOI 'app\static does not exist' (gap 3 lan):
+# ⚠️ GOC ROOT CUA MOI LOI 'app\\static does not exist' (gap 3 lan):
 # lenh `pyinstaller engine.spec` (entry-point script) KHONG them CWD vao
 # sys.path (chi `python -m PyInstaller` moi them). collect_data_files('app')
 # import package qua sys.path -> khong thay 'app' -> tra ve [] AM THAM ->
@@ -25,8 +25,8 @@ if _SPEC_ROOT not in _sys.path:
     _sys.path.insert(0, _SPEC_ROOT)
 
 # Assets cua app: bundle QUA IMPORT SYSTEM (collect_data_files) — an toan nhat.
-# Loai tru storage (57MB soundfonts/uploads — vo ich trong onefile, config.py
+# Loai tru storage (57MB soundfonts/uploads — vo ich, config.py da chuyen
-# da chuyen storage sang %APPDATA%\\SonicForgeDAW khi frozen) va __pycache__.
+# storage sang %APPDATA%\\SonicForgeDAW khi frozen) va __pycache__.
 datas = collect_data_files('app', excludes=['**/storage/**', '**/__pycache__/**', '**/*.pyc'])
 # Fallback cuoi cung: neu collect_data_files van tra ve rong (phong moi truong
 # hop ky la), dung datas TINH absolute — tinh huong xau nhat van co du assets.
@@ -41,49 +41,46 @@ datas += [
     (os.path.join(_SPEC_ROOT, 'md'), 'md'),   # /ai-prompt-generator (doc, ngoai package app)
 ]
 
-# librosa 0.11 dùng lazy_loader.attach_stub -> lúc RUNTIME cần file .pyi
+# ══════════════════════════════════════════════════════════════════════════
-# ton tai tren disk ('Cannot load imports from non-existent stub ...librosa\__init__.pyi').
+# TOI UU BUNDLE (SonicForgeStudio 1.1): 409MB -> ~120MB
-# PyInstaller mac dinh KHONG bundle .pyi -> phai collect explicit.
+# ──────────────────────────────────────────────────────────────────────────
-datas += collect_data_files('librosa', includes=['**/*.pyi'])
+# 1. librosa/numba/llvmlite (~171MB) + scikit-learn (~17MB) da DUOC LOAI BO
-
+#    khoi code (app/core/audio_features.py thay the, numpy/scipy/soundfile).
-# scipy >= 1.18 tach scipy.stats thanh nhieu module con (vd
+# 2. celery/kombu/billiard/redis (~40MB) KHONG bundle — desktop chay task
-# _ansari_swilk_statistics) import lazy ben trong ham -> hook scipy cua
+#    eager dong bo, khong can broker (app/api/v1/tasks.py da lazy + fallback).
-# PyInstaller miss -> ModuleNotFoundError luc runtime. Giai phap TRIET DE:
+# 3. scipy: KHONG con quet toan bo site-packages/scipy (truoc day bundle ca
-# scan FILESYSTEM toan bo site-packages/scipy (khong import, khong walk —
+#    scipy.stats/sparse/optimize/linalg ~48MB). Chi quet scipy.signal — goi
-# pkgutil.walk_packages BO QUA AM THAM subpackage import loi luc build,
+#    lazy-import noi bo cua no van duoc bat day du (scipy.signal.windows,
-# da gap: may user mat ca cay scipy.sparse.csgraph._shortest_path).
+#    _savitzky_golay, _spectral_py... duoc import bang ten ben trong ham).
-# Bat moi module .py + C-extension .pyd/.so -> hiddenimports day du.
+#    scipy.signal la goi DUY NHAT con duoc app dung (sub_tab_dsp,
+#    render_engine, audio_features).
+# ══════════════════════════════════════════════════════════════════════════
 import importlib.util as _ilu
 import glob as _glob
-_scipy_spec = _ilu.find_spec('scipy')
-_scipy_dir = os.path.dirname(os.path.abspath(_scipy_spec.origin))
-_scipy_hidden = []
-for _ext in ('*.py', '*.pyd', '*.so'):
-    for _f in _glob.glob(os.path.join(_scipy_dir, '**', _ext), recursive=True):
-        _rel = os.path.relpath(_f, _scipy_dir)
-        _base = os.path.basename(_rel).split('.')[0]  # bo .cpython-312-x86_64... .so
-        _pkg = os.path.dirname(_rel).replace(os.sep, '.')
-        _mod = ('scipy.' + _pkg + '.' + _base) if _pkg else ('scipy.' + _base)
-        if _mod not in _scipy_hidden:
-            _scipy_hidden.append(_mod)
-# Cung co bang hiddenimport TINH: _morestats import module nay o top-level
-# (scipy 1.18+); collect_submodules du phong nhung neu miss (version khac
-# tren may user) thi dong nay van dam bao bundle co.
-if 'scipy.stats._ansari_swilk_statistics' not in _scipy_hidden:
-    _scipy_hidden.append('scipy.stats._ansari_swilk_statistics')
-# scipy.sparse.csgraph cung lazy-import C-extension tu ben trong ham (vd
-# _shortest_path, _traversal, _matching) — hiddenimport tinh phong walk miss.
-for _m in ('scipy.sparse.csgraph._shortest_path', 'scipy.sparse.csgraph._traversal',
-           'scipy.sparse.csgraph._matching', 'scipy.sparse.csgraph._min_spanning_tree'):
-    if _m not in _scipy_hidden:
-        _scipy_hidden.append(_m)
 
-a = Analysis(
+def _scan_pkg_modules(pkg_name: str):
-    ['desktop_engine.py'],
+    """Scan filesystem cua 1 package con (khong import, khong walk) ->
-    pathex=[_SPEC_ROOT],
+    bat moi module .py/.pyd/.so -> hiddenimports day du, tranh lazy-import miss."""
-    binaries=binaries,
+    _spec = _ilu.find_spec(pkg_name)
-    datas=datas,
+    if _spec is None or _spec.origin is None:
-    hiddenimports=collect_submodules('celery.fixups') + _scipy_hidden + [
+        print(f"WARN: khong tim thay package '{pkg_name}' - bo qua scan")
+        return []
+    _pkg_dir = os.path.dirname(os.path.abspath(_spec.origin))
+    _out = []
+    for _ext in ('*.py', '*.pyd', '*.so'):
+        for _f in _glob.glob(os.path.join(_pkg_dir, '**', _ext), recursive=True):
+            _rel = os.path.relpath(_f, _pkg_dir)
+            _base = os.path.basename(_rel).split('.')[0]  # bo .cpython-312-x86_64... .so
+            _sub = os.path.dirname(_rel).replace(os.sep, '.')
+            _mod = (pkg_name + '.' + _sub + '.' + _base) if _sub else (pkg_name + '.' + _base)
+            if _mod not in _out:
+                _out.append(_mod)
+    return _out
+
+_scipy_signal_hidden = _scan_pkg_modules('scipy.signal')
+
+# Uvicorn lazy-load loop/protocol theo ten (string) -> hiddenimport tinh.
+_hidden = [
     'uvicorn.logging',
     'uvicorn.loops',
     'uvicorn.loops.auto',
@@ -96,11 +93,35 @@ a = Analysis(
     'soundfile',
     'sf2utils',
     'mido.backends.rtmidi',
-    ],
+] + _scipy_signal_hidden
+
+# Khoa khong bundle: loai toan bo cay nang khong con duoc dung.
+_excludes = [
+    'tkinter',
+    # libs da thay the (audio_features.py)
+    'librosa', 'numba', 'llvmlite', 'sklearn', 'scikit-learn',
+    'joblib', 'threadpoolctl', 'audioread', 'lazy_loader', 'soxr',
+    # celery/redis chi dung cho server (Docker), khong cho desktop
+    'celery', 'kombu', 'billiard', 'vine', 'amqp', 'redis',
+    'click_didyoumean', 'click_plugins', 'click_repl',
+    # LUU Y: KHONG exclude 'click' — uvicorn.main import click (CLI parser)!
+    'dateutil', 'pytz', 'tzdata', 'msgpack', 'yaml',
+    # khong dung trong desktop
+    'matplotlib', 'pandas', 'IPython', 'jupyter', 'pytest', 'setuptools',
+    # keo vao nham boi hooks_contrib (app khong import bao gio)
+    'PIL', 'Pillow', 'cairosvg', 'zstandard', 'imageio',
+]
+
+a = Analysis(
+    ['desktop_engine.py'],
+    pathex=[_SPEC_ROOT],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=_hidden,
     hookspath=[],
     hooksconfig={},
     runtime_hooks=[],
-    excludes=['tkinter'],
+    excludes=_excludes,
     win_no_prefer_redirects=False,
     win_private_assemblies=False,
     cipher=None,

src-tauri/tauri.conf.json

@@ -25,7 +25,7 @@
   },
   "bundle": {
     "active": true,
-    "targets": ["msi"],
+    "targets": ["nsis", "msi"],
     "icon": [
       "icons/32x32.png",
       "icons/128x128.png",