What offline speech recognition is
Offline speech recognition converts spoken audio into text entirely on your own device. A neural network runs on your CPU (or GPU), takes your microphone audio, and outputs text — with no round trip to a server. Because nothing is uploaded, it works on a plane, in a secure facility, or on a flaky connection, and the audio never leaves the machine.
This is the opposite of cloud STT (Google Speech-to-Text, AWS Transcribe, Otter, and most "voice typing" services), where your audio is streamed to a vendor's servers for processing.
How on-device ASR works
At a high level, every modern speech recognizer follows the same pipeline:
- Capture. The microphone records raw audio (typically 16 kHz mono for speech).
- Feature extraction. Audio is converted into a spectrogram — a numeric representation of frequencies over time.
- Acoustic + language modeling. A neural network maps the spectrogram to text. Whisper, for example, is a transformer trained on a huge multilingual dataset that does this end-to-end, handling punctuation and casing too.
- Decoding. The model produces the most likely text sequence, optionally with timestamps.
The key point for "offline": all four steps run from a model file already on disk. Once downloaded, the model needs no network. A 100% offline app like AirTypes simply bundles a Whisper model and runs this pipeline locally each time you speak.
Offline vs cloud speech-to-text
| Dimension | Offline (on-device) | Cloud |
|---|---|---|
| Privacy | ✅ Audio never leaves device | ❌ Audio sent to vendor |
| Works without internet | ✅ Always | ❌ No |
| Latency | Local — depends on your hardware | Network round-trip |
| Ongoing cost | Free after model download | Per-minute / subscription |
| Accuracy ceiling | High (large models) | High |
| Hardware needed | Your CPU/GPU + RAM | None (vendor's servers) |
The historical knock on offline — worse accuracy — no longer holds at the larger model tiers. The remaining trade-off is that you spend local compute and RAM instead of money per minute.
The offline ASR engine landscape
- OpenAI Whisper — the dominant open model. Excellent accuracy, ~99 languages, robust to noise and accents. The default choice in 2026.
- whisper.cpp — a fast C/C++ port of Whisper that runs efficiently on CPU and Apple Silicon. It's what makes Whisper practical in lightweight desktop apps.
- faster-whisper — a CTranslate2-based reimplementation that's significantly faster and lighter on memory, popular for real-time use.
- Vosk — a compact, low-latency toolkit that runs on tiny devices (even Raspberry Pi and phones). Lower ceiling than Whisper but great for embedded/streaming.
- Coqui STT / DeepSpeech — older open engines (DeepSpeech is now largely succeeded). Still seen in legacy projects.
- NVIDIA NeMo / Parakeet — high-accuracy models for those with NVIDIA GPUs and a tolerance for heavier setup.
For a desktop dictation workflow, Whisper (via whisper.cpp or faster-whisper) is almost always the right engine. Vosk wins when you need ultra-low latency or to run on minimal hardware.
Accuracy vs model size
Whisper ships in tiers, and the choice is a direct speed-vs-accuracy dial:
- Tiny / Base (~40–150 MB) — fastest, lowest RAM, more errors on names and jargon. Good for quick notes on weak hardware.
- Small / Medium — the sweet spot for most laptops: strong accuracy, reasonable speed.
- Large-V3 (~1.5 GB+) — the highest accuracy, best for technical vocabulary and accents, at the cost of speed and memory.
Practical rule: pick the largest model your machine runs comfortably in real time. On modern CPUs that's usually Small or Medium; with a capable GPU or Apple Silicon, Large becomes viable.
When offline is the right choice
- Privacy & compliance — legal, medical, finance, and government work where audio cannot be sent to a third party. See our enterprise offline voice recognition guide.
- No or poor connectivity — travel, fieldwork, air-gapped systems.
- Cost at scale — heavy daily dictation where per-minute cloud pricing adds up.
- Latency control — no network variance; speed depends only on your hardware.
From an engine to a usable app
An engine isn't a workflow. Whisper transcribes a file; it doesn't capture your microphone on a hotkey, clean up filler words, and type text at your cursor in any application. That last mile is what a dictation app provides.
AirTypes wraps Whisper into exactly that: hold a hotkey, speak, and the transcribed text appears at your cursor — system-wide, offline, on macOS and Linux (Windows in development).
- Want to actually get set up? Start with the practical offline voice-to-text guide.
- Want to compare finished apps? See the best offline speech recognition apps comparison.
- On Linux? See offline voice to text for Linux.
FAQ
What is offline speech recognition?
It converts speech to text entirely on your device, with no audio sent to a server. A neural model runs locally, so it works with no internet and keeps audio private. It's also called on-device ASR or offline STT.
Is offline speech recognition accurate?
Yes. Models like Whisper match leading cloud services at the larger sizes. Accuracy scales with model size — smaller models are faster but make more mistakes; larger models are more accurate but slower.
What is the best offline speech recognition engine?
For most uses, Whisper (via whisper.cpp or faster-whisper) gives the best accuracy-to-effort ratio and broad language support. Vosk is best for lightweight, low-latency, or embedded scenarios.
Does Whisper work offline?
Yes. Once the model file is downloaded, Whisper runs fully offline. Apps like AirTypes bundle it so transcription happens locally with no cloud calls.
Run offline speech recognition without the setup
AirTypes bundles Whisper into a system-wide, 100% offline dictation app. Free for 7 days.
Download AirTypes