type

project_id

private_key_id

private_key

client_email

client_id

auth_uri

token_uri

auth_provider_x509_cert_url

client_x509_cert_url

unique_bucket_name

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

# Sometimes errors are okay
The last few lessons were a deep dive into squeezing every last drop of error out and producing transcriptions with the highest accuracy. But quality is in the eye of the beholder.



# Only keywords matter
I consulted with a data scientist who was creating software for a major retailer.  The retailer's goal was **not** to create super accurate transcriptions.

The business need was focused on [sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) and product insight. This means that **some words** were much more important than others, and achieving low rates of error did not align with the desired outcomes.


This retailer wanted to answer questions like
* What products are people having the most trouble with?
* For legal compliance, do the customer service agents always say "this call may be recorded"?
* Are some scripts that agents follow generating more frustration for customers than other scripts?

Learn how to identify when to use WER and when not to.

Use Case Matters

Getting Started

Your First Program

Recognition Configuration

Speech Adaptation

Models

Word Error Rate WER

Final Thoughts

Appendix

Use Case Matters

Sometimes errors are okay

Only keywords matter