Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Project that allows one to use a microphone with OpenAI whisper.
| Date | Stars |
|---|---|
| 2026-07-24 | 788 |
| 2026-07-25 | 788 |
| 2026-07-28 | 788 |
| 2026-07-30 | 788 |
| 2026-08-06 | 788 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Whisper Mic This repo is based on the work done [here](https://github.com/openai/whisper) 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](https://youtu.be/S58MGCU7Wgg) An older video tutorial for this repo can be seen [here](https://www.youtube.com/watch?v=nwPaRSlDSaY) ### Professional Assistance If are in need of paid professional help, that is available through this [email](mailto:[email protected]) ## 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. ```python 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](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.
Excerpt of 3,284 characters
Read on GitHub
Blake · Brillibits
52
1
1
1
1
1
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6b4b1e872778acd7, topic:speech-recognition, topic:whisper, topic:speech-to-text