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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

# Listen to audio
Download and listen to these two audio files:

# Transcript needs improvement
The transcripts below were produced with the unchanged quickstart code.

<table>
<tbody>
<tr>
<td> <b>call1_short.mp3</b></td>
<td style="text-align:left"> 

**API results**

**Warren Avenue** number **to** Gillette Wyoming 82716 **76802 / 60**

**Human transcription**

What's your shipping address? 310 Lauren Avenue #2, Gillet Wyoming, 82716.  How about your phone number? Um 207-680-2068</td>
</tr>


</tbody>
</table>
<table>
<tbody>
<td > 

**call2_short.mp3**

</td>
<td style="text-align:left">

**API Results**

**6539 and Blanco information** 59714

**Human transcription**

Thank you for calling AT&T my name is Erica can I have your wireless number that you are calling about today? 406-509-1202. That number was 406-509-1202 is that correct?  Please verify some information for me first. Please verify the billing zip code. 59714.  Also the last four digits of your social security number. 5528.  Thanks so much for that sir.

</td>
</tr>

</tbody>
</table>


# Transcript needs improvement
The transcripts below were produced with the unchanged quickstart code.

<table>
<tbody>
<tr>
<td> <b>call1_short.mp3</b></td>
<td> 

**API results**

**Warren Avenue** number **to** Gillette Wyoming 82716 **76802 / 60**

**Human transcription**

What's your shipping address? 310 Lauren Avenue #2, Gillet Wyoming, 82716.  How about your phone number? Um 207-680-2068</td>
</tr>


</tbody>
</table>
<table>
<tbody>
<td > 

**call2_short.mp3**

</td>
<td>

**API Results**

**6539 and Blanco information** 59714

**Human transcription**

Thank you for calling AT&T my name is Erica can I have your wireless number that you are calling about today? 406-509-1202. That number was 406-509-1202 is that correct?  Please verify some information for me first. Please verify the billing zip code. 59714.  Also the last four digits of your social security number. 5528.  Thanks so much for that sir.

</td>
</tr>

</tbody>
</table>


Learn how to improve transcription with speech adaptation classes.

Classes

Getting Started

Your First Program

Recognition Configuration

Speech Adaptation

Models

Word Error Rate WER

Final Thoughts

Appendix

Classes

Listen to audio

Transcript needs improvement