With voice-controlled touchpoints becoming more and more the norm in human-computer interactions, our Speech-to-Text (STT) API is a great option for developers looking to build voice into their applications. The API processes over 1 billion spoken minutes of speech each month, enough to transcribe all Presidential inauguration speeches in U.S. history over 1 million times. Our customers use STT for everything from auto-generating captions, to generating insights to improve sales calls, to powering robots that help with childhood development.  

With Speech-to-Text, you can accurately convert speech into text with several adaptations including:

• Model Customization - customize for domain-specific terms
• Speech Adaptation - provide context to influence results and formatting
• Diarization - separate speakers on different channels or automatically detect when speakers change
• Profanity Filtering - configure your request to detect profane words and edit them out of the transcript

Whether you’re using our pre-trained APIs for the first time or you’re a seasoned AI veteran, our codelabs are great resources for practicing and getting even more comfortable with our pre-trained models. In addition to helping you brush up on your skills, Codelabs also provide step-by-step instructions for how to set up your GCP project and get a $300 credit if you need it. They’ll also walk you through everything else you need to get your sample up and running, such as authentication, and  installing the client libraries and tooling like the Cloud Shell Editor. 

That’s why we’ve decided to round up some our top Speech codelabs, to help you get the most of our Speech-to-Text API, and our Text-to-Speech API as well: 

1. Using the Speech-to-Text API with Python lab and C# lab 

Speech-to-Text is easy to get started with; in the code snippet below you can see all you need is the client library, an audio file and a few lines of code to get a transcript created: