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Lead the GenAI revolution by incorporating Google’s Speech-to-Text AI in Python. Learn use cases, execute demos, master recognition configuration, and improve transcription accuracy. Future-proof your skills.

mysdk.tar.gz

transcription demo code

key storing

lady susan - copy audio

Punctuation 

Multi language

Multi audio

Diarization

Diarization-copy

Speech adaptation phrases

Speech adaptation classes

Speech adaptation boost

Speech adaptation boost-tuning

Recognition model

Enhanced model

More than one minute

Welcome! My name is Bruce Bookman and I’m a subject matter expert in Conversational AI at Google. In this course, I will show you how to incorporate Google’s powerful Speech-to-Text Artificial Intelligence models into a Python program.
Google Speech-to-Text enables you to convert audio to text by applying neural network models in an easy-to-use API. So, in this course, you will start by understanding the main use cases for Speech-to-Text (STT) and an overview of the API.
You will then execute some demo code for the API to create a transcription for an audio file. Don’t worry, you’ll run through each line of code to make sure you’ve got it down.
In the following chapters, you will focus on recognition configuration, speech adaptation, and the different models used for speech recognition. Lastly, you will learn about word error rate and how to measure transcription accuracy.
By the end of this course, you will be able to inject STT in your own Python projects and you will have a great new skill for your resume.

Google Cloud: AI Speech-to-Text with Python 3

## Prerequisites
This course is written for a Linux environment.

The course is designed for **intermediate** to **advanced** Python programmers. Beginners may find it difficult to follow along, as the lessons assume a certain level of expertise:

* Previous experience using the Linux command line and terminal-based editors
* Beginner-level experience with files and file formats, specifically for audio files
* Experience with Python3 lists and dictionaries
* A beginner-level familiarity with JSON is helpful 
* Functioning speakers on your computer (or headphones)


**NOTE:** If at any time during the course you run into errors or need extra assistance, the Appendix contains a lesson on Troubleshooting, along with video demonstrations and other supporting information.


The next lesson provides a quick demonstration of the Speech-to-Text API.

# Prerequisites
This course is written for a Linux environment.

The course is designed for **intermediate** to **advanced** Python programmers. Beginners may find it difficult to follow along, as the lessons assume a certain level of expertise:

* Previous experience using the Linux command line and terminal-based editors
* Beginner-level experience with files and file formats, specifically for audio files
* Experience with Python3 lists and dictionaries
* A beginner-level familiarity with JSON is helpful 
* Functioning speakers on your computer (or headphones)


The course is designed for those with Intermediate or Advanced experience with Python 3.  

Getting Started

Your First Program

Recognition Configuration

Speech Adaptation

Models

Word Error Rate WER

Final Thoughts

Appendix

Prerequisites

Prerequisites