VoiceStudio/docs/install/troubleshooting.md

OmniVoice Studio — Install Troubleshooting

The top 10 errors users have actually hit on v0.2.x, with their causes and fixes. Most have a deeplink anchor that the in-app error UI's "Open docs for this error" button targets directly.

Start here: self-diagnosis

Before digging through the entries below, let the app diagnose itself:

• In the app: Settings → About → "Run self-check" verifies your compute device (CUDA/MPS/CPU), ffmpeg, HuggingFace token, disk space, data-directory permissions, RAM, installed TTS engines, and hub reachability — each with a hint when something's off.

• Headless / terminal:

  uv run python backend/main.py --diagnose          # same checks, exits 1 on failure
  uv run python backend/main.py --diagnose --deep   # also loads the active engine
                                                    # and synthesizes a test utterance
  

  --deep catches "installed but broken" engines. On a fresh install it may cold-load the model (minutes, plus a large download).

• Filing an issue? Settings → About → "Save diagnostic bundle" produces a zip (self-check report, recent classified errors, scrubbed log tails) you can drag straight onto the GitHub issue. Home paths and anything token-shaped are redacted before they leave your machine.

1. pkg_resources missing (ModuleNotFoundError)

Symptom: the splash screen shows ModuleNotFoundError: No module named 'pkg_resources' during WhisperX import, and the app never advances past the "Setting up models" step.

Cause: WhisperX (and a couple of its transitive deps) still imports pkg_resources, which setuptools >= 80 dropped. pyproject.toml pins setuptools>=75,<80 so it stays present — but the venv can still lose it two other ways: (a) antivirus (commonly Windows Defender) quarantines pkg_resources' files, or (b) a partial/interrupted extract. In both cases setuptools' metadata remains, so uv/pip report it "already satisfied" and a plain install no-ops — the files are never restored.

Fix: in the backend venv, force a reinstall (a plain install won't work for the reasons above):

uv pip install --reinstall 'setuptools>=75,<80'

then restart. If it recurs, your antivirus is removing the files again — add the backend .venv folder to its exclusions (Windows Security → Virus & threat protection → Exclusions). The app's auto-repair now uses --reinstall too, so a fresh install heals itself.

Linked issues: #58, #248

1a. Model load fails: [Errno 2] No such file or directory: '…/transformers/…/modeling_*.py'

Symptom: the System Check / model load fails with e.g. [Errno 2] No such file or directory: '…/site-packages/transformers/models/qwen3/modeling_qwen3.py'.

Cause: same class as §1 — a corrupted/incomplete transformers install. A model load lazily resolves a module file that's missing from site-packages (an interrupted uv sync, antivirus quarantine, or a partial update). The package's metadata is intact, so a plain install no-ops and never restores the file. Restarting does not help (the file is still gone).

Fix: force-reinstall transformers in the backend venv, then restart:

uv pip install --reinstall transformers

Or, as a quick workaround, switch ASR to faster-whisper in Settings → Models. If it recurs, add the backend .venv to your antivirus exclusions (see §1). Newer builds classify this error and show the reinstall hint directly instead of a bare path + "try restarting".

2. HF 401 / pyannote license not accepted

Symptom: dubbing fails with HfHubHTTPError: 401 Client Error: Unauthorized for url …pyannote/speaker-diarization-3.1…, or diarization silently falls back to a single speaker.

Cause: pyannote/speaker-diarization-3.1 is a gated model — even with a valid HF token, you need to accept the model's license on its HuggingFace page before the token works for downloads.

Fix:

1. Open Settings → API Keys in the app and paste a working HF token (or set HF_TOKEN in your env). See docs/setup/huggingface-token.md.
2. Visit https://huggingface.co/pyannote/speaker-diarization-3.1 while signed in with the same HF account → click "Agree and access repository".
3. Retry the job. The token state in Settings → API Keys should now show the "App" row with a green check next to your username.

Linked issue: #35

3. Gatekeeper quarantine on macOS

Symptom: "OmniVoice Studio.app is damaged and can't be opened."

Cause: the app is not yet notarised (signing is wired in release.yml and activates once the maintainer adds the Apple cert secrets) — until then macOS quarantines every download.

Fix: see macos.md#gatekeeper-quarantine.

4. AppImage white screen on Fedora 44 / Ubuntu 24.04

Symptom: the AppImage window opens fully white. No UI ever appears.

Cause: WebKitGTK 2.44 / 2.46 compositing-mode regression.

Fix: see linux.md#appimage-white-screen-on-fedora-44--ubuntu-2404.

5. Windows Triton / torch.compile OOM

Symptom: the first synthesis call fails with OutOfMemoryError: CUDA out of memory or RuntimeError: Triton compilation failed, especially on <16 GB VRAM GPUs.

Cause: the engine's torch.compile step compiles Triton kernels with a peak memory footprint that exceeds free VRAM. Windows-only quirk.

Fix: see windows.md#torch-compile-oom.

Linked issue: #65

6. uv venv Python download fails (restricted network)

Symptom: during first launch, uv exits with a network error pulling python-build-standalone from GitHub. Common in China, intermittently in Russia, sometimes on corporate proxies.

Fix: see linux.md#restricted-networks-china--russia (same env vars work on macOS and Windows — UV_PYTHON_INSTALL_MIRROR, UV_HTTP_TIMEOUT=120, UV_HTTP_RETRIES=5, UV_PYTHON_PREFERENCE=only-system).

Linked issues: #57, #60.

7. .deb ffprobe path conflict on upgrade

Symptom: after upgrading from a pre-v0.3 .deb, ffprobe -version reports "OmniVoice bundled ffprobe" instead of the system ffmpeg, breaking other apps that rely on /usr/bin/ffprobe.

Fix: see linux.md#deb-ffprobe-conflict.

8. Docker LAN access — media preview 404

Symptom: OmniVoice loads on http://<lan-ip>:3900 but the audio preview pane shows 404s for /media/....

Cause: pre-v0.3, the frontend hardcoded localhost:3900 for media-preview URLs, which is wrong when the UI is reached from a different LAN host.

Fix: the frontend derives its API/media base from the page's own origin. When running behind a reverse proxy where the UI and API are on different origins, set the runtime override OMNIVOICE_PUBLIC_API_BASE (works on the prebuilt image via docker run -e) — see docker.md#lan-access.

9. Apple Silicon mlx-whisper unavailable on Intel mac

Symptom: on an Intel mac, OmniVoice logs mlx-whisper backend unavailable; falling back to faster-whisper.

Cause: mlx-whisper and mlx-audio only build for arm64 (Apple Silicon).

Fix: none needed on Apple Silicon setups that log this transiently. Note that Intel Macs can no longer run the local backend at all — PyTorch dropped Intel-Mac wheels, so this entry only applies to historical installs (see macos.md and #889).

10. Windows: Could not locate cudnn_ops_infer64_8.dll during transcription

Symptom: on Windows + NVIDIA, transcription/dubbing fails and the backend log shows Could not locate cudnn_ops_infer64_8.dll. Settings → Models shows WhisperX or faster-whisper selected.

Cause: WhisperX and faster-whisper run on CTranslate2, which needs cuDNN 8, but PyTorch 2.8 ships cuDNN 9. OmniVoice side-loads a cuDNN-8 copy from .venv\Lib\site-packages\cudnn8_compat\ — but the step that installs that folder only ever lived in the dev-loop setup script, which isn't bundled into the packaged app. Packaged installs never had these libraries at all, so reinstalling never fixed it (#827).

Fix: update to the latest build and relaunch — the app's bootstrap now detects a CUDA machine and installs the cuDNN-8 libraries into the backend venv automatically at launch (#869). (The check is skipped — and its negative result cached — on CPU/AMD/Apple machines, so non-NVIDIA launches stay instant.)

If the automatic install can't run (offline / restricted network), install manually into the backend venv, then restart:

uv pip install --no-deps --python .venv\Scripts\python.exe --target .venv\Lib\site-packages\cudnn8_compat nvidia-cudnn-cu12==8.9.7.29

(On Linux the target is .venv/lib/pythonX.Y/site-packages/cudnn8_compat.)

Or sidestep cuDNN 8 entirely: switch the ASR backend to PyTorch Whisper in Settings → Models. It runs on PyTorch's own stack (cuDNN 9, bundled with torch) and needs no cuDNN-8 DLL — it loads its Whisper pipeline on demand (no extra env var).

11. IndexTTS / CosyVoice / ChatterboxTTS clash

Symptom: installing one of these engines breaks the others — e.g. after installing CosyVoice, IndexTTS errors out with import conflicts.

Cause: these engines pin incompatible transformer / torch versions inside their own engine venvs. Pre-v0.3 they shared a single venv.

Fix: Phase 2 ships subprocess isolation per engine (each engine runs in its own venv). For v0.3, workaround: install only one of the conflicting engines per OmniVoice copy. See docs/engines/cosyvoice.md for the dedicated CosyVoice path.

Linked issue: #55

12. CUDA PyTorch wheel download fails on first run

Symptom: first-run setup stops at Installing dependencies with a failure that mentions torch and a download.pytorch.org (or download-r2.pytorch.org) URL — e.g. Failed to download torch==2.8.0+cu128 …win_amd64.whl. The app then won't launch.

Cause: on Windows/Linux NVIDIA machines, OmniVoice installs the CUDA PyTorch build (torch + torchaudio) from PyTorch's own index. That CUDA wheel is large (~2.5 GB), so a flaky or restricted network drops it partway. This is a download/network problem, not a bug in OmniVoice — but the CUDA wheels come from a named, explicit index that a PyPI mirror (UV_DEFAULT_INDEX) cannot redirect, so the generic mirror trick doesn't help here.

Fix, in order:

1. Clean & Retry. Large downloads frequently succeed on a second attempt — OmniVoice already retries each request 5× with long timeouts, and a fresh attempt restarts cleanly.
2. Use a VPN if your network throttles or blocks the PyTorch CDN.
3. Provide the wheels manually (offline path). Download the two wheels that match your machine from a source you can reach (the official pytorch.org wheel index or a regional mirror), then drop them in the wheel folder and Clean & Retry — OmniVoice will install from your local copies instead of the network:
   • Folder: <env dir>/wheels (the exact path is printed in the error message and in the setup log; <env dir> is your chosen install/storage location).
   • Files: the torch and torchaudio wheels for your exact Python/OS/CUDA — e.g. torch-2.8.0+cu128-cp311-cp311-win_amd64.whl and the matching torchaudio-2.8.0+cu128-cp311-cp311-win_amd64.whl. They must match the pinned versions (shown in the failing URL).
   • On retry, OmniVoice re-resolves the install using those local wheels; the rest of the (small) dependencies still come from PyPI/your mirror.

If you don't have an NVIDIA GPU, you don't need the CUDA build at all — a CPU / Apple-Silicon install skips this index entirely.

Linked issue: #569

13. Stuck on the download page / incomplete model cache ("only refs/")

Symptom: the setup screen never finishes the model download and you can't reach the main app. Looking in the HF cache, a model folder (models--k2-fsa--OmniVoice, models--Systran--faster-whisper-large-v3) has refs/ and maybe config.json but no weight files (blobs/ empty or tiny).

Cause: the download started but the large weight shards never finished — almost always the connection dropping, throttling, or being blocked mid-pull (corporate/school proxy, VPN, antivirus quarantining the multi-GB file, or a region where huggingface.co is slow/blocked). The app retries and verifies weights, but a connection that trickles rather than dies can stall for a long time.

Fix — force a clean re-download:

1. Fully quit OmniVoice. Check Task Manager (Windows) / Activity Monitor (macOS) and end any leftover omnivoice / python process — a half-running one keeps the cache locked.
2. Delete the incomplete model folder(s) entirely from the HF cache (the whole models--… folder, not just refs/). Leave other models alone:
   • models--k2-fsa--OmniVoice
   • models--Systran--faster-whisper-large-v3
3. Relaunch — the download page re-pulls from scratch.

If it stalls again at the same spot, the download is being blocked — try, in order:

• Antivirus/firewall — temporarily disable it for the download (large model files are a common false-positive quarantine), then re-enable.
• Connection — use a stable, direct connection; pause any VPN; avoid corporate/school networks.
• Region mirror — if huggingface.co is slow/blocked where you are, set a mirror before launching and relaunch:
  • macOS/Linux: export HF_ENDPOINT=https://hf-mirror.com
  • Windows (PowerShell): [Environment]::SetEnvironmentVariable("HF_ENDPOINT","https://hf-mirror.com","User")

Manual fallback (if downloads keep failing), pull the weights yourself into the same cache, then relaunch:

pip install -U "huggingface_hub[cli]"
huggingface-cli download k2-fsa/OmniVoice
huggingface-cli download Systran/faster-whisper-large-v3

(If OmniVoice uses a custom models directory, set HF_HOME to it first so the files land where the app looks.)

Newer builds detect an incomplete cache and re-offer the download instead of stranding you on this page — update once the fix is in your channel.

Linked issue: #622

14. "Can't reach the local backend" during generation / transcription / dubbing

Symptom: the app worked at startup (you reached the main menu and the model loaded), but the moment you generate audio, dub a video, transcribe, or dictate, it spins for a long time and then shows "Can't reach the local backend." The backend log ends right after a line like whisperx transcribing …tmpXXXX.wav (or a generate) with nothing after it — i.e. the backend is alive, the GPU job is what stalled.

Cause: this is not a connection, download, or "network mirror" problem — the backend started fine. A GPU job (a generate on the TTS model, or an ASR transcribe with WhisperX/faster-whisper large-v3) is too heavy for the available compute and runs for minutes; because it wedges its GPU-pool worker, every other request — including the next generate and the health check — is starved, which the UI surfaces as an unreachable backend. The usual trigger is VRAM starvation on NVIDIA: models contend for memory on an 8 GB-class GPU (the log shows e.g. GPU pool sized … 7.0 GB free). CPU-only machines hit the same wall on long clips. This is the same root cause whether the last thing you did was generate:start (audio), a dub, or a dictation.

There is no "Network → Restricted/Global mirror" toggle in Settings — that control (the footer/Sharing Network button) is for LAN sharing, not downloads. If someone pointed you there for this error, it was the wrong knob.

Fix — reduce ASR load (any one of these):

1. Pick a smaller ASR model / engine in Settings → Models — e.g. faster-whisper medium or small, instead of large-v3. Biggest win on low-VRAM GPUs.
2. Free VRAM: Flush the TTS model before dubbing so ASR isn't competing for memory, or
3. Run ASR on CPU (slower but reliable) if your GPU is small.
4. Test with a 10-second clip first — if that returns quickly, it confirms a compute/VRAM limit rather than a true hang.

Newer builds bound every GPU job — whole-file transcription, chunked dub transcription, and TTS generation: instead of hanging forever and starving the backend, a wedged job now fails after a timeout with this exact guidance, and the worker pool is reset so capacity is restored automatically (no app restart needed). Tune the bounds with OMNIVOICE_ASR_TRANSCRIBE_TIMEOUT_S (whole-file transcription) and OMNIVOICE_GENERATE_TIMEOUT_S (generation) — both in seconds, default 300 — and OMNIVOICE_TRANSCRIBE_CHUNK_TIMEOUT_S (per-chunk dub transcription, default 120). Raise them for very long single files/generations, lower them to fail faster on a small machine.

If transcribe timeouts keep repeating back-to-back, pool resets aren't recovering the underlying hang — the wedged thread keeps its VRAM until the app exits. The error message will then recommend switching the ASR engine to Faster-Whisper (crash-isolated subprocess) (faster-whisper-isolated) in Settings → Engines: it runs transcription in a separate process that can be force-killed to reclaim a hung transcribe and its VRAM, at a small per-call overhead. It reuses your existing faster-whisper install (nothing extra to download). OmniVoice never switches engines automatically — this stays your call.

15. Stuck at "preparing" forever after a crash / BSOD (Windows)

Symptom: after an unclean shutdown (Windows BSOD, forced power-off), every launch sits on the "preparing" splash indefinitely — even though the backend is actually healthy (its log shows models loaded, and http://127.0.0.1:3900/health answers {"status":"ok"} in a browser). The WebView log contains:

IPC custom protocol failed, Tauri will now use the postMessage interface instead
TypeError: Failed to fetch

Cause: the crash corrupted the WebView2 profile cache at %LOCALAPPDATA%\com.debpalash.omnivoice-studio\EBWebView. Both the IPC custom protocol and its postMessage fallback break, so the splash never hears the "ready" signal from the app shell (issue #879).

Fix: current builds handle this automatically — if the splash gets no IPC signal within ~10 s it checks the backend over plain HTTP and proceeds on its own; if the backend isn't up either, after ~45 s a recovery panel appears with Repair and restart (Windows), which clears the WebView cache and relaunches. Your voices, projects, and settings are not touched — only browser display data is cleared.

On older builds (≤ 0.3.8), or if the automatic repair fails, do it manually: quit OmniVoice Studio, delete the folder below, then start the app again.

Remove-Item -Recurse -Force "$env:LOCALAPPDATA\com.debpalash.omnivoice-studio\EBWebView"

Dub: "translation engine needs the optional … package"

Symptom: in the Dub tab, translating fails with e.g. "The 'google' translation engine needs the optional deep_translator Python package, which isn't installed in this backend."

Cause: the online translation engines (Google / DeepL / Microsoft / MyMemory via deep_translator, and the LLM provider via openai) are optional and not bundled. Only Argos and NLLB work out of the box.

Fix:

• From-source / Docker install: click the highlighted Install button next to the Engine label in the Dub tab (or run uv pip install deep_translator in the backend venv) and restart the backend.
• Packaged installer build: in-app install is disabled (read-only signed environment). Click the highlighted button to open the popover and Switch to Argos (bundled, offline) — or copy the command to run it in a from-source checkout.

Full guide: dubbing/translation-engines.md.

First-run setup fails on a restricted network (GitHub/PyPI blocked)

On networks that block or can't resolve GitHub, the first-run bootstrap may fail to download the managed Python (uv venv ... failed, often a DNS error). OmniVoice now tries, in order: the default GitHub host → a gh-proxy mirror → your system Python (if 3.11+ is installed). If all three fail:

1. Install Python 3.11+ from https://www.python.org/downloads/ (on Windows, tick "Add Python to PATH"), then relaunch — OmniVoice will use it.
2. Point at a reachable mirror for the Python download:
   • UV_PYTHON_INSTALL_MIRROR=https://gh-proxy.com/https://github.com/astral-sh/python-build-standalone/releases/download
3. Point at a PyPI mirror for the dependency install (uv sync):
   • China: UV_DEFAULT_INDEX=https://pypi.tuna.tsinghua.edu.cn/simple (or https://mirrors.aliyun.com/pypi/simple)
   • Fully-blocked networks (e.g. some regions): use a VPN — there is no government-blessed PyPI mirror to rely on.
4. The bootstrap already raises the network budget for you (UV_HTTP_TIMEOUT=120, UV_HTTP_CONNECT_TIMEOUT=30, UV_HTTP_RETRIES=5); you can raise them further in the environment if a mirror is very slow.

Linked issues: #130, #60, #57