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Introducing LeMUR, the easiest way to build LLM apps on spoken data

We've released LeMUR - our framework for applying LLMs to spoken data - for general availability. LeMUR is optimized for high accuracy on specific tasks:

  1. Custom Summary allows users to automatically summarize files in a flexible way
  2. Question & Answer allows users to ask specific questions about audio files and receive answers to these questions
  3. Action Items allows users to automatically generate a list of action items from virtual or in-person meetings

Additionally, LeMUR can be applied to groups of transcripts in order to simultaneously analyze a set of files at once, allowing users to, for example, summarize many podcast episode or ask questions about a series of customer calls.

Our Python SDK allows users to work with LeMUR in just a few lines of code:

# version 0.15 or greater
import assemblyai as aai

# set your API key
aai.settings.api_key = f"{API_TOKEN}"

# transcribe the audio file (meeting recording)
transcriber = aai.Transcriber()
transcript = transcriber.transcribe("")

# generate and print action items
result = transcript.lemur.action_items(
    context="A GitLab meeting to discuss logistics",
    answer_format="**<topic header>**\n<relevant action items>\n",


Learn more about LeMUR in our blog post, or jump straight into the code in our associated Colab notebook.