faster-whisper-hotkey 0.4.3

Push-to-talk transcription

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

• Models downloading: Missing models are automatically downloaded from Hugging Face.
• 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.

Current models

• (NEW) nvidia/canary-1b-v2:

  • 25 languages supported
  • Transcription and translation
  • No automatic language recognition
  • Crazy fast even on CPU in F16

• (NEW) nvidia/parakeet-tdt-0.6b-v3:

  • 25 languages supported
  • Transcription only
  • Automatic language recognition
  • Crazy fast even on CPU in F16

• mistralai/Voxtral-Mini-3B-2507:

  • English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian
  • Transcription only
  • Automatic language recognition
  • Smart (it even guesses when to put some quotes, etc.) and less error-prone for non English native speakers
  • GPU only

• Systran/faster-whisper:

  • Many languages
  • Transcription only

What I personally use currently?

- parakeet-tdt-0.6b-v3, on CPU, when I need all my VRAM to run my LMs

- Voxtral-Mini-3B-2507, on GPU, when I run smaller models and can fit it along them

Installation

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

From PyPi

• As a pip package:

  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
  

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 pip install .
  

• or as an uv tool:

  uv tool install .
  

For Nvidia GPU

You need to install cudnn https://developer.nvidia.com/cudnn-downloads

Usage

1. Whether you installed from PyPi or from source, just 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 or F8) 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

• voxtral: because of some limitations, and to keep the automatic language recognition capabilities, we are splitting the audio by chunks of 30s. So even if we can still transcribe long speech, best results are when audio is shorter than this. In the current state it seems impossible to concile long audio as 1 chunk and automatic language detection. We may need to patch upstream https://huggingface.co/docs/transformers/v4.56.1/en/model_doc/voxtral#transformers.VoxtralProcessor.apply_transcription_request

• Due to window type detection to send appropriate key stroke, unfortunately the VSCodium/VSCode terminal isn't supported for now. No clue if we can workaround this.

Tricks

• 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!

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
• 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