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Summarize your audio data

In this guide, you'll learn how to use LeMUR to summarize your audio data with key takeaways.


If you want a Quickstart, see Apply LLMs to audio files.

Before you start

To use LeMUR, you need an with a credit card set up.

With the LeMUR Task, you can send any prompt to the LLM and apply the model to your transcribed audio files.

To summarize the content in your audio data, define a summarization prompt and call transcript.lemur.task(). The underlying transcript is automatically used as additional context for the model.

Example output

The transcript describes several common sports injuries - runner's knee,
sprained ankle, meniscus tear, rotator cuff tear, and ACL tear. It provides
definitions, causes, and symptoms for each injury. The transcript seems to be
narrating sports footage and describing injuries as they occur to the athletes.
Overall, it provides an overview of these common sports injuries that can result
from overuse or sudden trauma during athletic activities

Summarization prompt examples

Try any of these prompts to get started:


Generate summaries for your audio data.
"Summarize key decisions and important points from the phone call transcript"

Summarize audio segments

Generate summaries for each segment or chapter of your audio data.
"Summarize the key events of each chapter"

For more use cases and prompt examples, see LeMUR examples.

Improve the results

To improve the results, see the following resources: