Hey 👋, this weekly update contains the latest info on new product features, tutorials, and our community.
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>>
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Build Talking AI ChatBot with ElevenLabs and OpenAI in 5 Minutes: Learn how to build an AI Audio Bot in Python using ElevenLabs, AssemblyAI & OpenAI.
Retrieval Augmented Generation on audio data with LangChain: Uncover the potential of Retrieval Augmented Generation (RAG) in enhancing LLM responses' relevance and clarity. Learn how to utilize RAG on audio data.