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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

## How to get the highest quality results

As someone who consults with Fortune 500 companies regularly, I notice that quality outcomes depend on a few best practices:

1. Hardware and audio capture technique matters. Beyond what the API can do, there are a lot of things that can be done to improve audio capture. Businesses should consult with an audio engineer.
2. Capture audio with a sampling rate of 16,000 Hz or higher.
3. To help determine the best configuration, test audio that represents the real world.
4. Invest time and money into configuration testing. Skipping this step can result in even more money and time wasted on poor transcription.
5. Test at least 1 hour of audio. 3 hours is better, 6 hours is great, and more than that is a case of diminishing returns.
6. Pay for professional human transcriptions for WER calculation purposes. Unless you work for a company full of trained transcriptionists, do not roll your own human transcriptions. If professionals have a 5% WER, imagine the errors introduced by everyday workers at your company.
7. The API models are trained with raw source audio. There is no need to up sample (convert 8000Hz file to 16000Hz, for example).
8. There is no payoff to the conversion of original audio from one encoding to another (MP3 to FLAC, for example).
9. There is no need to pre-process the audio to reduce noise or background music, as the models are trained for these situations.
10. If identifying separate speakers is critical, capture each audio on a different channel.


# How to get the highest quality results

As someone who consults with Fortune 500 companies regularly, I notice that quality outcomes depend on a few best practices:

1. Hardware and audio capture technique matters. Beyond what the API can do, there are a lot of things that can be done to improve audio capture. Businesses should consult with an audio engineer.
2. Capture audio with a sampling rate of 16,000 Hz or higher.
3. To help determine the best configuration, test audio that represents the real world.
4. Invest time and money into configuration testing. Skipping this step can result in even more money and time wasted on poor transcription.
5. Test at least 1 hour of audio. 3 hours is better, 6 hours is great, and more than that is a case of diminishing returns.
6. Pay for professional human transcriptions for WER calculation purposes. Unless you work for a company full of trained transcriptionists, do not roll your own human transcriptions. If professionals have a 5% WER, imagine the errors introduced by everyday workers at your company.
7. The API models are trained with raw source audio. There is no need to up sample (convert 8000Hz file to 16000Hz, for example).
8. There is no payoff to the conversion of original audio from one encoding to another (MP3 to FLAC, for example).
9. There is no need to pre-process the audio to reduce noise or background music, as the models are trained for these situations.
10. If identifying separate speakers is critical, capture each audio on a different channel.


In this lesson, we’ll cover the best practices that can lead to high-quality outcomes.

Success Is in Fine Details

Getting Started

Your First Program

Recognition Configuration

Speech Adaptation

Models

Word Error Rate WER

Final Thoughts

Appendix

Success Is in Fine Details

How to get the highest quality results