faster-whisper Hotkey

a minimalist push-to-talk style transcription tool built upon cutting-edge ASR models.

Hold the hotkey, Speak, Release ==> And baamm in your text field!

In the terminal, in a text editor, or even in the text chat of your online video game, anywhere!

Features

• User-Friendly Interface: Allows users to set the input device, transcription model, compute type, device, and language directly through the menu.
• Fast: Almost instant transcription, even on CPU when picking parakeet or canary.
• LLM correction (experimental): Optionally try to repair broken transcriptions text via any OpenAI-compatible API endpoint.

Current models

To help with choosing your model, you can see their AA-AgentTalk score which is particularly relevant for our use case.

• Qwen/Qwen3-ASR-1.7B-hf:

  • 30 languages
  • Transcription only
  • Automatic language recognition
  • CPU/GPU (a bit slow on CPU, as always, very fast on GPU)

• ibm-granite/granite-speech-4.1-2b:

  • 6 source languages (en, de, es, fr, ja, pt)
  • Transcription and translation (bidirectional to/from English, plus English→Italian and English→Mandarin)
  • No automatic language recognition
  • Autoregressive with punctuation and capitalization
  • CPU/GPU

• ibm-granite/granite-speech-4.1-2b-nar:

  • 5 languages
  • Transcription only
  • No automatic language recognition
  • Non-autoregressive --> faster than the above AR variant
  • No capitalization in output
  • CPU/GPU

• CohereLabs/cohere-transcribe-03-2026:

  • 14 languages
  • Transcription only
  • Officially, no automatic language recognition, but it still works pretty great
  • Runs well on CPU
  • Quite smart, deals well with hesitation and stutters

• nvidia/canary-1b-v2:

  • 25 languages
  • Transcription and translation (bidirectional to/from English)
  • No automatic language recognition
  • CPU/GPU Still usable on CPU

• nvidia/parakeet-tdt-0.6b-v3:

  • 25 languages
  • Transcription only
  • Officially, no automatic language recognition, but it still works pretty great
  • Crazy fast even on CPU

• mistralai/Voxtral-Mini-3B-2507:

  • 8 languages
  • Transcription only
  • Automatic language recognition
  • Smart (it even guesses when to put some quotes, etc.) and seems less error-prone for non English native speakers
  • GPU only

• Systran/faster-whisper:

  • Many languages
  • Transcription only
  • CPU/GPU

Installation

see https://docs.astral.sh/uv/ for more information on uv. uv is fast :)

Requires Python 3.11.14

From PyPi

• As a pip package:

  uv venv --python 3.11.14
  source .venv/bin/activate
  uv pip install faster-whisper-hotkey
  

• or as an tool, so that you can run faster-whisper-hotkey from any venv:

  uv tool install faster-whisper-hotkey --python=3.11.14
  

From source

1. Clone the repository:

   git clone https://github.com/blakkd/faster-whisper-hotkey
   cd faster-whisper-hotkey
   

2. Install the package and dependencies:

• as a pip package:

  uv venv --python 3.11.14
  source .venv/bin/activate
  uv pip install .
  

• or as an uv tool:

  uv tool install .
  

Usage

1. Run the tool using one of these methods:

   • If installed from PyPi or from source as a package/tool:

     faster-whisper-hotkey
     

   • Or using uv run (handles the venv and dependencies automatically):

     cd faster-whisper-hotkey
     uv run faster-whisper-hotkey
     

2. Go through the menu steps.

3. Once the model is loaded, focus on any text field.

4. Then, simply press the hotkey (PAUSE, F4, F8 or INSERT) while you speak, release it when you're done, and see the magic happening!

When the script is running, you can forget it, the model will remain loaded, and it's ready to transcribe at any time.

Configuration File

The script automatically saves your settings to ~/.config/faster_whisper_hotkey/transcriber_settings.json.

Limitations

• granite-nar: No punctuation or capitalization in output (by design of the non-autoregressive architecture). Use the autoregressive granite variant if you need punctuation.

• Using window type detection to send appropriate key strokes, we unfortunately can't see "sub windows". So for example, the VSCodium/VSCode terminal isn't supported for now. No clue if we can workaround this.

• Windows supported is not planned. That said, you can use eutychius's branch which seems working fine. See this comment for instructions.

Tips

• If you you pick a multilingual faster-whisper model, and select en as source while speaking another language it will be translated to English, provided you speak for at least few seconds.
• If you pick parakeet-tdt-0.6b-v3, you can even use multiple languages during your recording!
• For models whose weights are natively bfloat16 (the precision menu marks the native one), you can still pick float32: it upcasts the weights, which costs extra memory, but is far faster on CPUs without good native bf16 support. If your transcriptions feel slow on CPU, try float32.

Acknowledgements

Many thanks to:

• the developers of faster-whisper for providing such an efficient transcription inference engine
• NVIDIA for their blazing fast parakeet and canary models
• Mistral for their impressively accurate model Voxtral-Mini-3B model
• Cohere for their cohere-transcribe-03-2026 model
• IBM for their granite-speech-4.1 models
• Qwen for their Qwen3-ASR-1.7B model
• and to all the contributors of the libraries I used

Also thanks to wgabrys88 and MohamedRashadthat for their huggingface spaces that have been helpful!

And to finish, a special mention to @siddhpant for their useful broo tool, who gave me a mic <3