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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 as demo. This repo copies some of the README from the original project.

Video Tutorial

The latest video tutorial for this repo can be seen here

An older video tutorial for this repo can be seen here

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-mic

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_mic program. Use -h to see flag options.

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

Transcribing To A File

Using the command: whisper_mic --loop --dictate will type the words you say on your active cursor.

Usage In Other Projects

You can use this code in other projects rather than just use it for a demo. You can do this with the listen method.

from whisper_mic import WhisperMic

mic = WhisperMic()
result = mic.listen()
print(result)

Check out what the possible arguments are by looking at the cli.py file

Troubleshooting

If you are having issues, try the following:

sudo apt install portaudio19-dev python3-pyaudio

Contributing

Some ideas that you can add are:

  1. Supporting different implementations of Whisper
  2. Adding additional optional functionality.
  3. Add tests

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.