Tweepy VS deepgram-python-sdk

How is this much different than what tweepy does?

There are plenty of open source modules you can use, such as tweepy, which will make it a lot easier for you.

I chose Python as a language to handle the streaming, because a) it’s the language I’m strongest in, and b) because of tweepy.

· Tweepy. An easy-to-use Python library for accessing the Twitter API.

As announced in this blog post, the Twitter API v2 supports the ability to get metadata about edited Tweets. In this short guide, I will showcase how developers and researchers can get information about edited Tweets from the Twitter API v2 in Python using Tweepy.

In this tutorial, we will configure AWS CodePipeline to build an ECR image and deploy the latest version to lambda container. The application code will stream tweets using Tweepy, a Python library for accessing the Twitter API. First we need to setup CodePipeline and the various stages to deploy application code to lambda image which will stream tweets when invoked. The intended devops architecture is as below.

That means you need a requirements.txt file in your repo somewhere, and it needs to list the dependencies for your project. A good way of knowing what the dependencies are is to check what you imported at the beginning of your python file. For my bots, I need Tweepy, config, and Python-dotenv Requirements.txt looks like this for Divas, my Twitter Bot Collection.

you can easily make a simple script with tweepy that scrapes the data and puts it into whatever file you need

In order to use the Twitter API v2, you need to apply for a Twitter developer account. Once you have an approved Twitter developer account, follow the instruction here to obtain your BEARER TOKEN that you will use to connect to the Twitter API v2 in you code in Python. We will be using the Tweepy package in Python to get images from the Twitter API v2, so you will need to have Python as well as the Tweepy package installed on your machine. Instructions on installing the Tweepy package can be found in this tutorial.

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.