Faster Audio File Handling and Improved Error Messages

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⚡️Product Updates

Check out the AssemblyAI changelog for the latest product improvements made by our team:

• Faster transcription speed: Enhanced file handling has reduced latency by a minimum of 3 seconds across all audio files, with significant improvements for larger files from external URLs.
• More clarity for error messages: Refined for better understanding, especially during internal server errors.
• Rivet integration: Updated to be compatible with AssemblyAI's latest Node SDK.
• API status: Stay informed with real-time updates on our status page.

LeMUR: Build LLM apps on voice data

LeMUR is the easiest way to code applications that apply LLMs to speech. In just a few lines of code you can search, summarize, ask questions, and generate new text, on all of your audio and video. 

Check out the following popular cookbooks for using LeMUR:

• Customer Call Sentiment Analysis with LeMUR.
• Dialogue Data Extraction using LeMUR and JSON.
• Automatically Generate Action Items from a Meeting with LeMUR. 
• Sales Playbook Implementation with LeMUR.
• Audio File Processing with LLMs through LeMUR.
• Transcript Citation Extraction using OpenAI Embeddings.

Fresh From Our Blog

Combining Speech Recognition and Diarization in one model: A new approach towards multi-speaker speech processing integrates Speaker Diarization and Automatic Speech Recognition in a unified framework. We discuss the key insights from this recent exciting development in Speech AI research. Read more>>

How to use audio data in LangChain with Python: Learn how to integrate audio files seamlessly into LangChain. Develop robust LLM apps leveraging spoken data, all detailed in our step-by-step Python guide. Read more>>

Convert Speech to Text in Python in 5 Minutes: Learn how to perform Automatic Speech Recognition in 5 minutes using Python and the AssemblyAI Speech-to-Text API with this simple tutorial. Read more>>

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