Creating summarized chapters from podcasts
The Auto Chapters model summarizes audio data over time into chapters. Chapters makes it easy for users to navigate and find specific information. Each chapter contains the following:
- Summary
- One-line gist
- Headline
- Start and end timestamps
In this step-by-step guide, you’ll learn how to apply the model. You’ll send the auto_chapters
parameter in your request, and then use chapters
property from the response.
You can also learn the content on this page from Automatic Chapter Detection With AssemblyAI on AssemblyAI’s YouTube channel.
Get started
Before we begin, make sure you have an AssemblyAI account and an API key. You can sign up for a free account and get your API key from your dashboard.
The entire source code of this guide can be viewed here.
Here’s an audio example for this guide:
Step-by-step instructions
Python SDK
Python (requests)
TypeScript
PHP
Ruby
C#
Create a TranscriptionConfig
with auto_chapters
set to True
.
Python SDK
Python (requests)
TypeScript
PHP
Ruby
C#
Create a Transcriber
object and pass in the configuration.
Understanding the response
Your automatic chapters are located in the chapters
key of the API response. Each entry contains a summary
of the chapter, a one-line gist
and a chapter headline
, and start
and end
timestamps.
Conclusion
Creating text summaries using our Auto Chapters functionality works on all kinds of different input sources, not just podcasts. For example, you can use it to summarize lecture videos or other long-form content.
If you need more fine-grained control than Auto Chapters offers, you can use AssemblyAI’s Summarization model to customize the complexity of your summary.