deepgram-python-sdk VS black

ou have the perfect solution for a speech recognition provider, Deepgram. You get to coding, using their Python SDK.

Deepgram has a Python SDK that we can tap into that’s located on Github. We’ll also need to get started with an API key which we can grab in Console, a game-like hub in Deepgram to try the different types of transcriptions in many coding languages, including Python. When you first sign up, you'll get $150 in API credits to try out Deepgram's speech AI capabilities.

You’ll only need two Python libraries, PyAudio and websockets. The PyAudio library allows you to get sound from your computer’s microphone. The WebSockets Python library is used too since we’re working with live streaming. Deepgram also has a Python SDK but in this post, we’ll hit the API endpoint directly.

Python SDK - Official Python SDK for Deepgram's automated speech recognition APIs.

I also needed a speech-to-text API and leveraged Deepgram. We have a Python SDK I tapped into that made it super quick and easy to get up and running with the voice recognition transcription.

I needed to use an AI voice recognition API that would transcribe the podcast audio. That part was easy to figure out. Use the Deepgram Python SDK. I used the prerecorded option in this scenario to transcribe the already recorded audio. I also grabbed a Deepgram API key from our console, which has gamified missions you can try to get up to speed quicker.

The transcribe_with_deepgram() function comes from our Deepgram Python SDK, located here in Github.

Deepgram’s REST API - A speech-to-text API built on a 100% deep learning platform. It offers up to 90% translation accuracy with model training with no human validation necessary. I chose to write about it because the developer experience is excellent; should you want to extend the capabilities of this API further, you’ll find exactly what you need in Deepgram’s docs and tutorials.

Today I used Dolby.io and Deepgram to enhance an audio file and transcribe the speech of the interview into text. These two APIs seem like a great combination for many future projects!

To get started with our Deepgram Python SDK, you can visit our open-sourced Github repository here. The samples in the README.md will help you get up and running with real-time streaming and recorded audio. We also created special Python documentation for developers, which you can access here.

$ git commit -m "add pre-commit configuration" [INFO] Initializing environment for https://github.com/psf/black. [INFO] Installing environment for https://github.com/psf/black. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... black................................................(no files to check)Skipped [main 6e21eab] add pre-commit configuration 1 file changed, 7 insertions(+)

Black: Known as “The Uncompromising Code Formatter”, Black automatically formats your Python code to conform to the PEP 8 style guide. It takes away the hassle of having to manually adjust your code style.

black @ git+https://github.com/psf/black

In the realm of Python development, there is a multitude of code formatters that adhere to PEP 8 guidelines. Today, we will briefly discuss how to install and utilize black.

Perfect, that worked, thank you!

I thought this could be solved by changing the directory to src/ and then executing that command, but this didn't work.

This also seems to be an issue with the web app, e.g. the repository for the formatter black is only one white dot https://dep-tree-explorer.vercel.app/api?repo=https://github...

Ruff is not only much faster, but it is also very convenient to have an all-in-one solution that replaces multiple other widely used tools: Flake8 (linter), isort (imports sorting), Black (code formatter), autoflake, many Flake8 plugins and more. And it has drop-in parity with these tools, so it is really straightforward to migrate from them to Ruff.

What I am looking for is something like Black for Python, which is opinionated, with reasonable defaults, and auto-fixes most/all issues.

1. LICENSE: This file contains information about the rights and permissions granted to users regarding the use, modification, distribution, and sharing of the software. I already had an MIT License in my project. 2. pyproject.toml: It is a configuration file typically used for specifying build requirements and backend build systems for Python projects. I was already using this file for Black code formatter configuration. 3. README.md: Used as a documentation file for your project, typically includes project overview, installation instructions and optionally, contribution instructions. 4. example_package_YOUR_USERNAME_HERE: One big change I had to face was restructuring my project, essentially packaging all files in this directory. The name of this directory should be what you want to name your package and shoud not conflict with any of the existing packages. Of course, since its a Python Package, it needs to have an __init__.py. 5. tests/: This is where you put all your unit and integration tests, I think its optional as not all projects will have tests. The rest of the project remains as is.

using pylint and black ("the uncompromising code formatter")