The binary-filter hack from PR #13 broke Tauri's resource walker on
Linux:
resource path `.../dist/omnivoice-backend/_internal/libcufft.so.11`
doesn't exist
PyInstaller's accounting (hook-generated rerun manifests, resource
glob expansion) still referenced the files after they were filtered
out of `a.binaries`, so Tauri's build.rs saw a path that didn't
exist on disk. Removing the post-hoc filter drops that error.
Keep strip=True + optimize=2 — those alone should still shave hundreds
of MB from native libs + bytecode. If the CPU-only torch wheel (PR #11)
+ these two flags aren't enough to get under 2 GB on Linux/Windows,
the next step is splitting the backend into a separately-downloaded
payload rather than trying to force it into one installer asset.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* ci: opt JavaScript actions into Node 24 runtime
GH deprecates Node 20 for JavaScript actions on 2026-09-16. The
deprecation warning surfaces on every run right now. Setting
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24=true at workflow level makes
actions/checkout, actions/setup-*, astral-sh/setup-uv, and
oven-sh/setup-bun all run on Node 24 without bumping action versions.
This is a runtime override only — our own test script still pins
Node 22 via actions/setup-node@v4 (required for
--experimental-strip-types).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(release): strip symbols + filter CUDA/CUDA-provider binaries, report size
Previous slim pass (PR #11, CPU-only torch + module excludes) still left
the frozen backend above GH Releases' 2 GB per-asset cap. Two more levers:
1. strip=True on EXE + COLLECT. Strips debug symbols from ELF/Mach-O
native libraries. libtorch_cpu.so and friends drop ~25-30%. No-op on
Windows (MSVC stores symbols in separate .pdb files).
2. optimize=2 in Analysis. Compiles embedded bytecode with -OO:
docstrings + assertions removed. ~50-80 MB off the PYZ archive.
3. Post-hoc binary filter after collect_all. Even with nvidia wheels
excluded as Python modules, collect_all('torch')/('onnxruntime') can
still pull the CUDA-runtime shared libs via their linker hints.
Pattern-match them out of a.binaries before PYZ.
4. Log bundle size after freeze so CI runs can be compared without
downloading artifacts.
If this round still overshoots 2 GB, the next step is splitting the
payload (thin installer + post-install download of the Python bundle).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Linux .deb upload and Windows MSI build both hit GitHub Releases'
hard 2147483648-byte asset cap because the frozen backend was ~2.2 GB
on Linux/Windows. Root causes + fixes:
- PyPI's default torch/torchaudio wheels bundle the full CUDA runtime
(~1.8 GB of libcuda*, libcublas*, libcudnn*, libcufft*, libcusparse*,
etc.). We ship CPU-only inference from the desktop binary; GPU is
surfaced only when a user-installed driver is detected at runtime.
Re-install torch from download.pytorch.org/whl/cpu for the Linux and
Windows matrix jobs before PyInstaller freezes. macOS wheels don't
include CUDA so they skip this step.
- Expand backend.spec excludes: torch subpackages we never touch at
inference time (torch.distributed, torch._dynamo, torch._inductor,
torch._export, torch.testing, torch.onnx, torch.ao, torch.fx.
experimental, torch._functorch, torch.utils.tensorboard,
torch.utils.benchmark), torchaudio.prototype, and heavy pyproject
deps the backend never imports (gradio, tensorboardX, webdataset,
s3prl, funasr, pedalboard). Also drop test trees that collect_all
sweeps up (scipy.special.tests, numpy.f2py.tests, etc.).
Expected bundle size after trim: ~600-900 MB uncompressed on Linux /
Windows, well under the 2 GB cap for .deb and MSI.
Model weights were never bundled — they already download on first run
via the HF cache when the user hits the Dub / TTS / ASR flows. So no
user-visible behaviour changes; the app just ships without the libs
required for CUDA builds, which weren't callable on those runners
anyway.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
