NeuraType

Offline speech-to-text for Windows with a global hotkey. Press a key combo from any app, speak, press again — your words are transcribed and pasted automatically. Powered by OpenAI Whisper models, accelerated with faster-whisper (CTranslate2), running entirely on your machine.

Features

• Global hotkey dictation — transcribe into any app without switching windows
• File transcription — drag and drop or open any audio/video file
• Speaker detection — identifies and labels multiple speakers in file transcription (optional, see below)
• Local AI text processing — grammar correction, auto-formatting, filler word removal
• History — searchable log of all transcriptions
• Fully offline — no audio ever leaves your machine

Requirements

• Windows 10/11
• Python 3.11
• Administrator privileges (required for global hotkeys)
• ffmpeg — must be on your PATH
• A Whisper model file (see below)
• NVIDIA GPU recommended for fast transcription (CPU works but is slower)

Installation

1. Clone the repository

git clone https://github.com/your-username/neuratype.git
cd neuratype

2. Create a virtual environment

python -m venv venv
venv\Scripts\activate

3. Install dependencies

pip install -r pip-requirements.txt

For GPU acceleration (CUDA 12.1):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Skip the line above if you only have a CPU — the app will fall back automatically.

4. Choose a Whisper model

NeuraType uses the faster-whisper (CTranslate2) engine by default. Models are downloaded automatically on first use into the models/ folder — there is no manual download step. Just select a model in the app and start transcribing; the first run fetches it.

Model	Size	Notes
tiny	~39 MB	Fastest, basic accuracy
base	~74 MB	Fast, decent accuracy
small	~244 MB	Moderate speed, good accuracy
medium	~769 MB	Slower, very good accuracy
turbo	~1.5 GB	Recommended — best speed/accuracy
large	~1.5 GB	Slowest, best accuracy

Using the openai-whisper fallback engine? That engine uses OpenAI's original .pt model files instead of the CTranslate2 format. They also download automatically on first use, or you can drop a .pt file into the models/ folder manually. Switch engines with the engine setting (faster-whisper or openai-whisper).

5. Run the app

Right-click your terminal and choose "Run as administrator", then:

venv\Scripts\activate
python speech_to_text_app.py

Usage

Action	Default hotkey
Start / stop recording	Ctrl + Win
Cancel recording	Ctrl + Shift + Space
Show history	Ctrl + Shift + H
Repaste last transcription	Alt + Shift + Z
Refresh hotkeys	Ctrl + Alt + Z

1. Open any app and click into a text field.
2. Press the hotkey — a recording indicator appears.
3. Speak your message.
4. Press the hotkey again — transcription is processed and pasted.

All hotkeys are configurable from the app's settings panel.

Optional: Speaker Detection (File Transcription)

NeuraType can identify and label individual speakers in audio/video files, formatting output as a conversation:

Speaker 1: Hello, how are you?

Speaker 2: I'm doing well, thanks for asking.

This uses WhisperX and pyannote.audio. Setup is a one-time process:

Step 1 — Install WhisperX

venv\Scripts\pip install whisperx

Then reinstall CUDA torch (whisperx overwrites it with a CPU build):

venv\Scripts\pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps

Skip the second command if you are on CPU only.

Step 2 — Accept model licenses on Hugging Face

Create a free account at huggingface.co, then visit and click Agree on both of these pages:

• pyannote/speaker-diarization-3.1
• pyannote/segmentation-3.0

Step 3 — Generate a Hugging Face token

Go to huggingface.co/settings/tokens → New token → Role: Read → Generate.

Copy the token (starts with hf_...).

Step 4 — Enable in NeuraType

Open Settings → Speaker tab:

• Check Enable speaker detection
• Paste your token into the Hugging Face Token field
• Set Number of Speakers (or leave on Auto-detect)
• Click OK

The diarization models (~65 MB total) download automatically on the first file transcription. Subsequent runs are instant.

Speaker detection only applies to file transcription — microphone recording is unaffected.

Optional: OpenAI API key

Some features (grammar correction, context-aware formatting) use the OpenAI API. If you want these, add your key in the app's settings panel after first launch. The key is stored in settings.json, which is excluded from git.

Privacy

All transcription runs locally. No audio ever leaves your machine. The OpenAI API is only contacted if you explicitly enable AI-powered features. The Hugging Face token is used only to verify model license acceptance — no audio is sent to Hugging Face.

Troubleshooting

Hotkey does nothing — make sure you launched the app as Administrator.

keyboard library error on startup — same cause; run as Administrator.

Transcription is slow — install the CUDA version of torch (step 3 above) and use the turbo model.

Wrong microphone — select the correct input device from the dropdown in the app.

Auto-paste doesn't work — some apps block programmatic paste. Disable auto-paste in settings and copy from the app manually.

Speaker detection shows CPU mode after installing WhisperX — whisperx overwrites your CUDA torch. Run the force-reinstall command in Step 1 above, then restart the app.

Speaker detection 403 error — your Hugging Face token is missing or you haven't accepted the model licenses. Complete Steps 2–4 above.