Local voice input for developers: speak, transcribe locally, and paste at the cursor.
Linux Transcription Refinement License
Install • Demo • How it works • Status
What is WhisperType
Speak instead of typing.
WhisperType records speech, transcribes it locally with whisper.cpp, and pastes the result at the active cursor. The CLI alias remains vc.
The current primary path is Linux with an NVIDIA GPU. Optional Gemini refinement can remove dictation noise and restructure the transcription before it is pasted.
Proof available now: the working Linux implementation, a setup-check script, and the recorded demo below. There is no small one-command installer or prebuilt bundle today; setup still requires building whisper.cpp and downloading a model.
Quick start — Linux + NVIDIA GPU
Clone both repositories:
Build whisper.cpp with CUDA:
Download the model expected by the current Linux script:
Install Python dependencies:
Check the local setup:
Run:
Expected result: press F8, F9, or Right Ctrl to start/stop recording; the transcription is copied and pasted into the active application.
Ultra-Fast Engine: Parakeet 0.6B (vc2)
For sub-second, ultra-fast local transcription on NVIDIA GPUs, WhisperType supports parakeet.cpp running the NVIDIA Parakeet TDT 0.6B model (vc2).
Quick Setup (vc2)
(Or invoke via your vc2 shell alias).
Benchmark vs Whisper Medium (GPU)
Measured on NVIDIA RTX GPU using sequential audio dictation fixtures:
Engine Model Parameters Latency (30s audio) Speedup Accuracy (WER) Whisper.cpp medium.en 769M 4,821 ms 1.0x (baseline) High Parakeet.cpp (vc2) tdt-0.6b-v2-f16 600M 712 ms 6.77x faster Whisper Large v3 level
To run the live benchmark suite locally:
Demo
demo.mp4
The demo is evidence of the recorded workflow, not a benchmark of transcription accuracy or latency across hardware.
The problem
For long prompts, notes, and terminal instructions, typing can become the slowest part of getting an idea into an agent or editor. General dictation tools also tend to preserve filler words and transcription artifacts that are inconvenient in technical prompts.
WhisperType removes that specific friction by combining local speech-to-text with direct cursor insertion and optional post-processing.
How it works
The current Linux implementation:
records microphone audio with sounddevice;
invokes the locally built whisper-cli executable;
requires an NVIDIA GPU in the primary Linux path rather than silently falling back to CPU;
optionally sends the transcribed text, not the audio, to Gemini when refinement is enabled;
pastes with Ctrl+V or Ctrl+Shift+V depending on the focused application and VCPASTEMODE.
Configuration
Copy the example environment file:
For optional Gemini refinement:
Supported output-format values in the current Linux script:
Variable Values Purpose VCENABLELLM true / false Enable or disable Gemini post-processing VCLLMFORMAT plain / xml / json Shape of refined text GEMINIAPIKEY API key Required only when refinement is enabled VCPASTEMODE auto / ctrlv / ctrlshiftv Paste-key behavior
Without GEMINIAPIKEY, the Linux implementation falls back to the local Whisper transcription.
Current state
Implemented
Linux microphone recording and hotkey control.
CUDA-backed whisper.cpp transcription using ggml-medium.en.bin.
Clipboard insertion into the active application.
Terminal-aware paste-key selection on X11.
Optional Gemini text refinement with plain/XML/JSON output modes.
Local setup diagnostics in Linux/testsetup.sh.
Implemented but not continuously verified
The repository has local diagnostic/test scripts, but no CI currently proves end-to-end microphone → Whisper → refinement → paste behavior on a clean machine.
Desktop/window behavior depends on the local Linux environment. The terminal-detection helper is specifically X11-aware.
Planned / unresolved
Easier packaging and installation. The current dependency footprint makes a simple downloadable bundle impractical without additional packaging work.
Broader platform acceptance testing.
Multi-language support.
Additional refinement-provider options only when there is a maintained implementation path.
Not claimed as supported today
A one-command install.
A small standalone binary/package.
Verified macOS support.
Verified Windows end-to-end support.
CPU fallback in the primary Linux GPU script.
There are Windows-specific files in the repository, but this README does not present them as a verified release path without current acceptance evidence.
What sets the design apart
These are implementation choices, not novelty claims:
Local transcription: microphone audio is processed through local whisper.cpp.
Optional cloud refinement: only the transcription text is sent to Gemini when enabled.
Cursor-first workflow: output is intended to land directly where the developer is already typing rather than in a separate transcription UI.
Fail-closed GPU path: the current Linux GPU script explicitly checks for NVIDIA availability rather than silently changing execution mode.
Evals and test series
Run the setup diagnostic:
It checks Python, required Python imports, NVIDIA/CUDA availability, the compiled Whisper executable, and the expected model file.
What that proves: the required local pieces are present and the expected GPU executable/model paths exist.
What it does not prove: microphone quality, transcription accuracy, Gemini availability, paste behavior in every desktop environment, or cross-platform support.
Linux/testllmintegration.py targets an older Ollama/Qwen refinement path and should not be treated as verification of the current Gemini path.
The smallest useful missing test is an automated smoke test around a fixed WAV fixture that verifies Whisper output and the current refinement adapter without requiring live microphone input.
Example
This is an example of the refinement transformation, not a guaranteed output for every model response.
Future development
The main adoption blocker is installation, not another feature list. The next useful work is a smaller, reproducible distribution path with clean-machine verification before expanding the product surface.
License
MIT — see LICENSE.