is the core engine. You can build a GUI around it using frameworks like Qt or simple Win32. Key Advantage : Extremely fast inference and supports for optimized Intel CPU/GPU performance. Core Development Steps (Python Path) Set Up Your Environment
| Model | VRAM (GPU) | RAM (CPU) | Speed (1 hour audio) | Accuracy | |-------|------------|-----------|----------------------|-----------| | tiny | ~1 GB | ~2 GB | 5–10 min | Good for clean speech | | base | ~1 GB | ~3 GB | 10–15 min | Better | | small | ~2 GB | ~4 GB | 20–30 min | Great for podcasts | | medium| ~3 GB | ~6 GB | 40–60 min | Excellent | | large | ~5 GB | ~10 GB | 90–120 min | Best (near human) | whisper gui windows
: For high-performance needs, whisper.cpp has various community-built GUIs that run natively on Windows without heavy dependencies. Performance Comparison Speed (Relative) Accuracy (WER) OpenAI Whisper Faster-Whisper Batched Faster-Whisper Data sourced from Mobius Labs. is the core engine
: If you have an NVIDIA graphics card, look for versions that support CUDA . This will make transcription significantly faster than using your CPU alone. Model Selection : When the GUI asks you to pick a model: Base/Tiny : Extremely fast, but makes more mistakes. Core Development Steps (Python Path) Set Up Your