Deepgram API: Accurate and Fast Speech to Text Transcription
Introduction and Overview Deepgram is an end-to-end deep learning speech to text API that stands out from the rest with its super accuracy, speed, and affordability. The mission of Deepgram is "every voice heard and understood," and it has the potential to revolutionize the way we interact with audio files. In this blog post, we'll explore how to get started with the Deepgram API and use it to transcribe oral histories, particularly those of underrepresented voices.
Getting Started with Deepgram To begin using the Deepgram API, you'll need to create a Deepgram account and API key. This can be done by signing up with Google and selecting the projects you want to work on. Once you have your API key, you can start using the API to transcribe audio files.
Setting Up the Project
To interact with the Deepgram API, you'll need to use the Node JS SDK, which can be installed using npm install @deepgram/client. Create a new file called index.js and import the Deepgram SDK. You'll also need to set up a .env file to store your API key, which can be hidden using an extension like Cloak.
Using the Deepgram API The sample file provided by Deepgram has clear instructions in the comments, making it easy to get started with the API. The API has various features, including punctuation, diarization, and language support. You can pass in options to the API, such as the source file, language, and features you want to use.
Project Example: "Black at WVU" Bekah is using the Deepgram API to transcribe oral histories for a project called "Black at WVU," which focuses on telling the stories of black people at West Virginia University. This project is a great example of how the Deepgram API can be used to give a voice to underrepresented communities.
Code Setup and Execution To get started with the project, you'll need to set up a file structure that includes a server.js file for the Express server and a client folder with an index.html file. You'll also need to configure the Parcel bundler to serve the client-side code and set up the server to run on port 3000.
Transcription and Results Once you've set up the project, you can execute the code to transcribe the audio file using the Deepgram API. The results of the transcription will include the transcript and its accuracy. You can also specify different channels and their potential uses in the transcription process.
Future Development In the future, it's possible to create a web app to display the transcription results. This can be done using Express to create the web app and Parcel bundler to serve the client-side code. A simple example of how to use Parcel bundler with Express can be created to get started.
Conclusion The Deepgram API is a powerful tool for speech to text transcription, and it has the potential to revolutionize the way we interact with audio files. With its accuracy, speed, and affordability, it's an ideal solution for projects that require high-quality transcription. Whether you're working on a project like "Black at WVU" or something entirely different, the Deepgram API is definitely worth exploring.