Frequently Asked Questions¶
General¶
What video formats does WhisperJAV support?¶
Any format FFmpeg can read: MP4, MKV, AVI, MOV, WMV, FLV, WAV, MP3, FLAC, M4A, M4B, and many more. If FFmpeg can extract audio from it, WhisperJAV can process it.
How long does transcription take?¶
Depends on video length, pipeline, and GPU:
| Video Length | Faster (GPU) | Balanced (GPU) | Ensemble (GPU) | Faster (CPU) |
|---|---|---|---|---|
| 30 min | ~1 min | ~2 min | ~4 min | ~10 min |
| 2 hours | ~3 min | ~5 min | ~10 min | ~40 min |
These are rough estimates. Actual times vary with GPU model and audio complexity.
Can I use WhisperJAV without a GPU?¶
Yes. Go to Advanced → check "Accept CPU-only mode". Use Faster mode for best CPU speed. It works, just significantly slower.
Quality¶
Which pipeline gives the best subtitles?¶
For anime/JAV content: ChronosJAV with anime-whisper, or Ensemble (Balanced + Qwen3-ASR with Smart Merge) for maximum accuracy.
For general Japanese content: Balanced mode is the best single-pass option.
The subtitles have hallucinated text (random English phrases, URLs, etc.)¶
This is a known Whisper behavior on silent or very quiet sections. WhisperJAV includes a hallucination sanitizer that removes most of these. Try:
- Use Aggressive sensitivity (captures more speech, leaving less "silence" for hallucinations)
- Use Ensemble mode (two passes catch different hallucinations)
- Enable a Speech Enhancer (ClearVoice or BS-RoFormer) to clean the audio first
The timing is off / subtitles appear too early or late¶
Try a different Scene Detector:
- Semantic (default) — best for most content
- Auditok — better for content with clear silence between dialogue
- Silero — more aggressive splitting
Or try ChronosJAV pipeline, which uses TEN VAD for tighter timing.
Translation¶
Which translation provider is best?¶
For cost-effectiveness: DeepSeek offers excellent quality at low cost, especially for CJK languages.
For quality: Claude or GPT-4 produce the most natural translations, but at higher cost.
For privacy: Local LLM runs entirely on your machine with no data leaving your computer.
Translation fails with "API token limit" error¶
Your subtitle batches are too large for the model's context window. Try:
- Reduce Max Batch Size in Advanced Settings (try 15 or 10)
- Use a model with a larger context window
- WhisperJAV v1.8.6+ auto-caps batch size for local LLMs
Translation shows "Unknown provider: Gemini" on Linux¶
Install the missing dependency: pip install google-api-core. This is fixed in v1.8.6+.
Installation¶
The installer is stuck / taking very long¶
The first install downloads ~3-5 GB of packages. On slow connections this can take 30+ minutes. Check the install log in the installation directory for progress.
"CUDA not available" but I have an NVIDIA GPU¶
- Verify your NVIDIA driver version:
nvidia-smi - Driver 450+ required for CUDA 11.8, 570+ for CUDA 12.8
- You do NOT need to install the CUDA Toolkit — PyTorch bundles its own
- Try reinstalling PyTorch with the correct CUDA version
How do I upgrade?¶
Or for code-only (faster): whisperjav-upgrade --wheel-only
See the Upgrade Guide for details.
Troubleshooting¶
Processing fails with an error¶
- Check the Console at the bottom of the GUI for the error message
- Enable Debug logging in Advanced tab and retry — check
whisperjav.log - Try a simpler pipeline (Faster mode) to isolate the issue
- Report the error on GitHub Issues with the error message and your system details
The GUI won't start¶
- Windows: Ensure WebView2 is installed (comes with Windows 10 1803+ and Windows 11)
- Linux: Install
libwebkit2gtk-4.0-dev(Ubuntu) orwebkit2gtk4.0-devel(Fedora) - macOS: WebKit is built-in, should work automatically
- Try launching from command line (
whisperjav-gui) to see error output