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Whisper Mic

This repo is based on the work done here by OpenAI. This repo allows you use use a mic to run scripts. This repo copies some of the README from original project.

Video Tutorial

See the video tutorial for this repo here. This is a fork of here the video may not be relevant

Professional Assistance

If are in need of paid professional help, that is available through this email

Setup

Now a pip package!

  1. Create a venv of your choice.
  2. Run pip install whisper-voice-commands

Example usage

whisper-voice-commands --model tiny --script_path ~youruser/scripts/ --english --ambient --dynamic_energy

Check whisper-voice-commands --help for more details

Available models and languages

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.

Size Parameters English-only model Multilingual model Required VRAM Relative speed
tiny 39 M tiny.en tiny ~1 GB ~32x
base 74 M base.en base ~1 GB ~16x
small 244 M small.en small ~2 GB ~6x
medium 769 M medium.en medium ~5 GB ~2x
large 1550 M N/A large ~10 GB 1x

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Microphone Demo

You can use the model with a microphone using the whisper-voice-commands program. Use -h to see flag options.

Some of the more important flags are the --model and --english flags.

Troubleshooting

If you are having issues with the cli.py not running try the following:

sudo apt install portaudio19-dev python3-pyaudio

Contributing

Currently, this is just a cli demo. I forsee that this pip package could become more than that for example:

from whisper_mic.mic import WhisperMic
mic = WhisperMic(timeout=5)
command = mic.listen()

License

The model weights of Whisper are released under the MIT License. See their repo for more information.

This code under this repo is under the MIT license. See LICENSE for further details.

Thanks

Until recently, access to high performing speech to text models was only available through paid serviecs. With this release, I am excited for the many applications that will come.

Todo

port to a faster platform like faster whisper or whisper.cpp and get it running on the pinephone