So for context, I have an accent and AI can't understand me very well. This is a challenge with Zoom meetings where Otter.ai can't understand half of what I'm saying, or even something as simple as voice commands for my TV. I want to, without reinventing the wheel, take an existing model similar to what Otter uses, and then give it extra training with my labeled voice data (like I speak into my microphone and give it the "answer" on what I said). How would you go about doing this? I'm thinking I might start looking on hugging face, but the options are just endless and I don't even know where to begin. Help please? (This could go on my portfolio if successful, or even put it out into production for others to train the model on their own voices/accents) EDIT: I opted to try using Azure's built-in model and retrain it with my voice data instead of building something from scratch.

Pre-commit hooks are automated scripts or checks that run before you commit your code to a version control system (VCS) like Git. They force you and your collaborators to write clean and consistent code. Reference: https://pre-commit.com/ To set up pre-commit you first need to have Git installed and run on your Python console the usual: pip install pre-commit Once installed, place in your root a file named .pre-commit-config.yaml and fill it with a collection of 'hooks'. ```yaml - repo: https://github.com/pre-commit/mirrors-black rev: stable hooks: - id: black ```yaml The hooks are the scripts responsible for the checks, there are many and way more evil than this. Have fun and let everyone know how frustrating is to work with you🫡 https://pre-commit.com/hooks.html

Hey guys! Ever feel like you're drowning in tutorial hell, trying to figure out where all your Python installations and paths are leading? 😵‍💫 How do you keep track of it all without losing your mind?

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