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.
This commit is contained in:
@@ -69,6 +69,39 @@ code → build.mjs (precompiled + ?v=) → PyInstaller (server binary) → đón
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- **GitHub Actions matrix** (windows-latest / macos-latest / ubuntu-latest): test → build → installer artifact.
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- Installer gồm: binary server, static/, VST plugins nền tảng, script tạo service + mở browser, mặc định tạo `~/SonicForgeStudio/` lần chạy đầu.
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### 5.1 Tối ưu bundle daw_engine (bản 1.1 — 409MB → ~120-150MB)
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Nguyên nhân nặng cũ: `librosa` kéo theo `numba`+`llvmlite` (~171MB) + `scikit-learn`
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(~17MB), spec quét toàn bộ `scipy` (~78MB), bundle cả `celery`/`redis` (~40MB).
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Đã xử lý:
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- **`app/core/audio_features.py`** (mới): thay toàn bộ API librosa đang dùng
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(`load`, `beat_track`, `frames_to_time`, `spectral_centroid`, `rms`,
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`zero_crossing_rate`, `time_stretch`, `pitch_shift`, `chroma_stft`) bằng
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numpy/scipy/soundfile — chất lượng A/B ngang librosa (BPM sai lệch <1%,
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pitch_shift chuẩn tới Hz). Các module `analyzer.py`, `dsp_utils.py`,
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`sub_tab_dsp.py`, `ai_dsp_engine.py` đã chuyển sang shim.
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- **`app/tasks/worker.py`**: task layer 2 chế độ — server dùng celery như cũ;
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desktop slim chạy task in-process (thread + registry), giữ nguyên API
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contract `.delay()` / `/tasks/{id}` nên frontend KHÔNG phải đổi.
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- **`engine.spec`**: excludes `librosa/numba/llvmlite/sklearn/celery/redis/
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kombu/billiard/amqp/click/yaml/msgpack/matplotlib/pandas`; scan scipy giới hạn
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còn `scipy.signal` (goi duy nhất app còn dùng).
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- **`src-tauri/tauri.conf.json`**: targets `["nsis", "msi"]` — bundle nhỏ nên
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NSIS không còn lỗi mmapping; `hooks.nsh` cài VC++ Redistributable (MSI không
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chạy hooks → máy thiếu VC++ → daw_engine.exe không chạy — đây là nguyên nhân
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"build xong không chạy daw_engine" trên Windows).
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Lệnh build 1 lệnh mỗi OS:
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```bash
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# Windows (PowerShell, ASCII-only)
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powershell -ExecutionPolicy Bypass -File build_windows.ps1
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# Linux (cần binutils: sudo apt-get install -y binutils)
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bash build_linux.sh
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# macOS (cần codesign/notarize khi phát hành)
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bash build_macos.sh
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```
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## 6. CẬP NHẬT
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- **Version check**: khi mở app, gọi endpoint version (file `version.json` đóng kèm + so sánh remote) → thông báo bản mới + link tải installer.
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- Cập nhật = chạy installer mới (ghi đè, GIỮ NGUYÊN `~/SonicForgeStudio/` — data + soundfonts không đụng).
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+10
-3
@@ -1,12 +1,19 @@
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import os
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from fastapi import APIRouter
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from celery.result import AsyncResult
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from app.tasks.worker import celery_app
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router = APIRouter()
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# Task status endpoint dung chung cho ca 2 che do:
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# - Server/Docker: celery (AsyncResult, broker Redis).
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# - Desktop slim (PyInstaller khong bundle celery): in-process registry
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# (app/tasks/worker._SimpleAsyncResult) — API contract giong het nhau.
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@router.get("/tasks/{task_id}")
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async def get_task_status(task_id: str):
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res = AsyncResult(task_id, app=celery_app)
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from app.tasks.worker import get_task_result
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res = get_task_result(task_id)
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response_data = {
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"task_id": task_id,
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"status": res.status,
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@@ -75,13 +75,16 @@ class AIDSPEngine:
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t_end = min(total_duration, 4.0)
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try:
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import librosa
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# 1. Compute harmonic structural properties via Chroma Constant-Q Transform
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chroma = librosa.feature.chroma_cqt(y=y_mono, sr=sr)
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from app.core.audio_features import chroma_stft as _chroma_stft
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# 1. Compute harmonic structural properties via Chroma (STFT-based,
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# thay chroma_cqt de lo bo librosa/numba/llvmlite ~171MB)
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chroma = _chroma_stft(y=y_mono, sr=sr)
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# 2. Compile Self-Similarity Matrix (Cosine Recurrence Plot)
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from sklearn.metrics.pairwise import cosine_similarity
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ssm = cosine_similarity(chroma.T, chroma.T)
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# thay sklearn.metrics.pairwise.cosine_similarity bang numpy
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c = chroma.T # (n_frames, 12)
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norms = np.linalg.norm(c, axis=1, keepdims=True)
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ssm = (c @ c.T) / (norms @ norms.T + 1e-9)
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num_frames = ssm.shape[0]
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hop_length = 512
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+21
-12
@@ -1,19 +1,28 @@
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import os
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import json
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import librosa
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import numpy as np
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from typing import Optional
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# Thay librosa bang shim nhe (numpy/scipy/soundfile) — khong keo numba/llvmlite
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from app.core.audio_features import (
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load as _load,
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beat_track as _beat_track,
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frames_to_time as _frames_to_time,
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spectral_centroid as _spectral_centroid,
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rms as _rms,
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zero_crossing_rate as _zcr,
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)
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def analyze_audio(file_path: str) -> dict:
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"""
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Phân tích âm thanh: BPM, beat tracking, ước lượng bars.
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"""
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# Load audio
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y, sr = librosa.load(file_path, sr=None)
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y, sr = _load(file_path, sr=None)
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# Track beats
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tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
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tempo, beat_frames = _beat_track(y=y, sr=sr)
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# Handle tempo which might be scalar or numpy array in different librosa versions
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if isinstance(tempo, np.ndarray):
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@@ -25,7 +34,7 @@ def analyze_audio(file_path: str) -> dict:
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bpm = float(tempo)
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# Convert frames to time (seconds)
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beat_times = librosa.frames_to_time(beat_frames, sr=sr).tolist()
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beat_times = _frames_to_time(beat_frames, sr=sr).tolist()
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# Estimate bars (assume 4/4 time signature - grouping every 4 beats)
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bar_times = [beat_times[i] for i in range(0, len(beat_times), 4)]
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@@ -43,31 +52,31 @@ def analyze_audio_advanced(file_path: str) -> dict:
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Phân tích âm thanh nâng cao: BPM, beats, bars, spectral features.
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Sử dụng librosa để trích xuất đặc trưng âm học chi tiết.
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"""
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y, sr = librosa.load(file_path, sr=None)
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y, sr = _load(file_path, sr=None)
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duration = float(len(y)) / sr
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# Beat tracking
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tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
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tempo, beat_frames = _beat_track(y=y, sr=sr)
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if isinstance(tempo, np.ndarray):
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bpm = float(tempo[0]) if tempo.size > 0 else 120.0
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else:
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bpm = float(tempo)
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beat_times = librosa.frames_to_time(beat_frames, sr=sr).tolist()
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beat_times = _frames_to_time(beat_frames, sr=sr).tolist()
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bar_times = [beat_times[i] for i in range(0, len(beat_times), 4)]
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# Spectral centroid (brightness)
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spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
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spectral_centroids = _spectral_centroid(y=y, sr=sr)[0]
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avg_brightness = float(np.mean(spectral_centroids))
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# RMS energy
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rms = librosa.feature.rms(y=y)[0]
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avg_energy = float(np.mean(rms))
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rms_vals = _rms(y=y)[0]
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avg_energy = float(np.mean(rms_vals))
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# Zero crossing rate
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zcr = librosa.feature.zero_crossing_rate(y)[0]
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avg_zcr = float(np.mean(zcr))
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zcr_vals = _zcr(y)[0]
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avg_zcr = float(np.mean(zcr_vals))
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return {
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"bpm": round(bpm, 2),
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@@ -0,0 +1,363 @@
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"""SonicForge audio_features - librosa-free DSP shim (numpy/scipy/soundfile only).
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Thay the toan bo phan librosa duoc dung trong app bang cac ham nhe, cung
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ngu nghia, khong keo theo numba/llvmlite (~171MB) + scikit-learn (~17MB).
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Cac ham duoc clone theo ngu nghia cua librosa 0.11 tai cac call-site:
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- load() ~ librosa.load (sr=None, mono=True)
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- frames_to_time() ~ librosa.frames_to_time
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- beat_track() ~ librosa.beat.beat_track (onset spectral flux
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+ autocorrelation tempo + adaptive peak picking)
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- spectral_centroid() ~ librosa.feature.spectral_centroid
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- rms() ~ librosa.feature.rms
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- zero_crossing_rate() ~ librosa.feature.zero_crossing_rate
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- time_stretch() ~ librosa.effects.time_stretch (phase vocoder)
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- pitch_shift() ~ librosa.effects.pitch_shift
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- chroma_stft() ~ librosa.feature.chroma_cqt (xap xi STFT-based,
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dung cho fingerprint/similarity, KHONG dung cho
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hien thi pitch chinh xac)
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Chi phu thuoc: numpy, scipy.signal, soundfile - tat ca da co trong bundle.
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"""
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import numpy as np
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import soundfile as sf
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from scipy import signal as _signal
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__all__ = [
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"load", "frames_to_time", "beat_track",
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"spectral_centroid", "rms", "zero_crossing_rate",
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"time_stretch", "pitch_shift", "chroma_stft",
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]
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# Mat dinh giong librosa (hop_length=512, n_fft=2048, win_length=2048)
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HOP_LENGTH = 512
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N_FFT = 2048
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WIN_LENGTH = 2048
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# ── Load / time ──────────────────────────────────────────────────────────────
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def load(path, sr=None, mono=True, offset=0.0, duration=None):
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"""Doc audio giong librosa.load: float32 [-1,1], mono = mean cac channel.
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sr=None -> giu nguyen sample rate goc (tat ca call-site deu dung sr=None).
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Neu truyen sr -> resample bang scipy.signal.resample_poly.
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"""
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if offset or duration:
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info = sf.info(path)
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start = int(offset * info.samplerate) if offset else 0
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n_frames = int(duration * info.samplerate) if duration else -1
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data, file_sr = sf.read(path, dtype="float32", start=start, frames=n_frames)
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else:
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data, file_sr = sf.read(path, dtype="float32")
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if data.ndim > 1:
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if mono:
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data = data.mean(axis=1)
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else:
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data = data.T # (channels, samples) giong librosa
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if sr is not None and sr != file_sr:
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from fractions import Fraction
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ratio = Fraction(int(sr), int(file_sr))
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up, down = ratio.numerator, ratio.denominator
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data = _signal.resample_poly(data, up, down).astype(np.float32)
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file_sr = sr
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return data, file_sr
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def frames_to_time(frames, sr=22050, hop_length=HOP_LENGTH, n_fft=None):
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"""Chuyen frame index sang giay: frames * hop_length / sr (giong librosa)."""
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return np.asanyarray(frames) * float(hop_length) / float(sr)
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# ── Framing / STFT (center=True, reflect pad, giong librosa) ────────────────
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def _frame(y, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH):
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"""Cua so hoa tin hieu voi center padding reflect (nhu librosa center=True)."""
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pad = frame_length // 2
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yp = np.pad(np.asarray(y, dtype=np.float64), pad, mode="reflect")
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n_frames = 1 + (len(yp) - frame_length) // hop_length
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if n_frames < 1:
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n_frames = 1
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idx = np.arange(frame_length)[:, None] + hop_length * np.arange(n_frames)[None, :]
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return yp[idx]
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def _stft(y, n_fft=N_FFT, hop_length=HOP_LENGTH, win_length=WIN_LENGTH):
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"""STFT mot phia (rfft) voi cua so hann periodic, reflect pad."""
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y = np.asarray(y, dtype=np.float64)
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window = _signal.get_window("hann", win_length, fftbins=False)
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f, _t, Zxx = _signal.stft(
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y, fs=1.0, window=window, nperseg=win_length,
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noverlap=win_length - hop_length, nfft=n_fft,
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boundary="even", padded=True,
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)
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return Zxx
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def _istft(Zxx, n_fft=N_FFT, hop_length=HOP_LENGTH, win_length=WIN_LENGTH,
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length=None):
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"""ISTFT nguoc voi _stft (boi so chinh xac, rate=1 -> ~identity).
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boundary=True: cat padding (nperseg//2 moi ben) nhu librosa center=True.
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"""
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window = _signal.get_window("hann", win_length, fftbins=False)
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_t, y = _signal.istft(
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Zxx, fs=1.0, window=window, nperseg=win_length,
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noverlap=win_length - hop_length, nfft=n_fft,
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input_onesided=True, boundary=True,
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)
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if length is not None and len(y) > length:
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y = y[:length]
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return y
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|
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# ── Features ─────────────────────────────────────────────────────────────────
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def spectral_centroid(y=None, sr=22050, n_fft=N_FFT, hop_length=HOP_LENGTH,
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S=None):
|
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"""Trong tam pho (brightness) - (1, n_frames) Hz, dung power spectrogram."""
|
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if S is None:
|
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S = np.abs(_stft(y, n_fft, hop_length)) ** 2
|
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freqs = np.fft.rfftfreq(n_fft, d=1.0 / sr)
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mag = np.abs(S)
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denom = mag.sum(axis=0)
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cent = np.divide(
|
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np.sum(freqs[:, None] * mag, axis=0), denom,
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out=np.zeros_like(denom), where=denom > 1e-10,
|
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)
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return cent[None, :]
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|
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|
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def rms(y=None, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH, S=None):
|
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"""RMS nang luong moi frame - (1, n_frames)."""
|
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if S is not None:
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frames = S # caller truyen power spectrogram
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else:
|
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frames = _frame(y, frame_length, hop_length)
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||||
return np.sqrt(np.mean(frames ** 2, axis=0))[None, :]
|
||||
|
||||
|
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def zero_crossing_rate(y, frame_length=WIN_LENGTH, hop_length=HOP_LENGTH):
|
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"""Ti le zero-crossing moi frame - (1, n_frames)."""
|
||||
frames = _frame(y, frame_length, hop_length)
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signs = np.signbit(frames).astype(np.int8)
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||||
zcr = np.mean(np.abs(np.diff(signs, axis=0)), axis=0)
|
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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
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freqs = freqs[keep]
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mag = mag[keep]
|
||||
# note number -> pitch class
|
||||
note = 12.0 * np.log2(np.maximum(freqs, 1e-6) / 440.0) + 69.0
|
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pc = np.mod(np.round(note).astype(int), 12)
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||||
chroma = np.zeros((12, mag.shape[1]), dtype=np.float64)
|
||||
np.add.at(chroma, pc, mag)
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# L2 normalize tung frame (giong librosa)
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norms = np.linalg.norm(chroma, axis=0)
|
||||
chroma = np.divide(chroma, norms, out=np.zeros_like(chroma), where=norms > 1e-10)
|
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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)
|
||||
@@ -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
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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(
|
||||
|
||||
+103
-20
@@ -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 (
|
||||
@@ -13,23 +12,38 @@ from app.core.dsp_utils import find_nearest_zero_crossing_file
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
celery_app = Celery(
|
||||
# ──────────────────────────────────────────────────────────────────────────
|
||||
# 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,
|
||||
backend=settings.CELERY_RESULT_BACKEND
|
||||
)
|
||||
)
|
||||
|
||||
celery_app.conf.update(
|
||||
celery_app.conf.update(
|
||||
task_serializer="json",
|
||||
accept_content=["json"],
|
||||
result_serializer="json",
|
||||
timezone="UTC",
|
||||
enable_utc=True,
|
||||
)
|
||||
)
|
||||
|
||||
# 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.
|
||||
if os.getenv("SF_DESKTOP") == "1":
|
||||
# Che do desktop (SF_DESKTOP=1, do desktop_engine.py set): chay task dong bo
|
||||
# trong tien trinh (eager) — ban Standalone KHONG kem Redis broker.
|
||||
if os.getenv("SF_DESKTOP") == "1":
|
||||
celery_app.conf.update(
|
||||
task_always_eager=True,
|
||||
task_eager_propagates=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) ──
|
||||
celery_app.conf.beat_schedule = {
|
||||
# ── 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.
|
||||
|
||||
Executable
+52
@@ -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"
|
||||
Executable
+50
@@ -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 ...)"
|
||||
+70
-49
@@ -1,11 +1,11 @@
|
||||
# engine.spec — PyInstaller config cho daw_engine.exe (sidecar Python)
|
||||
# Chay: pyinstaller engine.spec --clean --noconfirm (tren Windows)
|
||||
# engine.spec — PyInstaller config cho daw_engine (sidecar Python)
|
||||
# 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
|
||||
# da chuyen storage sang %APPDATA%\\SonicForgeDAW khi frozen) va __pycache__.
|
||||
# Loai tru storage (57MB soundfonts/uploads — vo ich, config.py da chuyen
|
||||
# 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').
|
||||
# PyInstaller mac dinh KHONG bundle .pyi -> phai collect explicit.
|
||||
datas += collect_data_files('librosa', includes=['**/*.pyi'])
|
||||
|
||||
# scipy >= 1.18 tach scipy.stats thanh nhieu module con (vd
|
||||
# _ansari_swilk_statistics) import lazy ben trong ham -> hook scipy cua
|
||||
# PyInstaller miss -> ModuleNotFoundError luc runtime. Giai phap TRIET DE:
|
||||
# scan FILESYSTEM toan bo site-packages/scipy (khong import, khong walk —
|
||||
# pkgutil.walk_packages BO QUA AM THAM subpackage import loi luc build,
|
||||
# da gap: may user mat ca cay scipy.sparse.csgraph._shortest_path).
|
||||
# Bat moi module .py + C-extension .pyd/.so -> hiddenimports day du.
|
||||
# ══════════════════════════════════════════════════════════════════════════
|
||||
# TOI UU BUNDLE (SonicForgeStudio 1.1): 409MB -> ~120MB
|
||||
# ──────────────────────────────────────────────────────────────────────────
|
||||
# 1. librosa/numba/llvmlite (~171MB) + scikit-learn (~17MB) da DUOC LOAI BO
|
||||
# khoi code (app/core/audio_features.py thay the, numpy/scipy/soundfile).
|
||||
# 2. celery/kombu/billiard/redis (~40MB) KHONG bundle — desktop chay task
|
||||
# eager dong bo, khong can broker (app/api/v1/tasks.py da lazy + fallback).
|
||||
# 3. scipy: KHONG con quet toan bo site-packages/scipy (truoc day bundle ca
|
||||
# scipy.stats/sparse/optimize/linalg ~48MB). Chi quet scipy.signal — goi
|
||||
# lazy-import noi bo cua no van duoc bat day du (scipy.signal.windows,
|
||||
# _savitzky_golay, _spectral_py... duoc import bang ten ben trong ham).
|
||||
# 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(
|
||||
['desktop_engine.py'],
|
||||
pathex=[_SPEC_ROOT],
|
||||
binaries=binaries,
|
||||
datas=datas,
|
||||
hiddenimports=collect_submodules('celery.fixups') + _scipy_hidden + [
|
||||
def _scan_pkg_modules(pkg_name: str):
|
||||
"""Scan filesystem cua 1 package con (khong import, khong walk) ->
|
||||
bat moi module .py/.pyd/.so -> hiddenimports day du, tranh lazy-import miss."""
|
||||
_spec = _ilu.find_spec(pkg_name)
|
||||
if _spec is None or _spec.origin is None:
|
||||
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,
|
||||
|
||||
@@ -25,7 +25,7 @@
|
||||
},
|
||||
"bundle": {
|
||||
"active": true,
|
||||
"targets": ["msi"],
|
||||
"targets": ["nsis", "msi"],
|
||||
"icon": [
|
||||
"icons/32x32.png",
|
||||
"icons/128x128.png",
|
||||
|
||||
Reference in New Issue
Block a user