Newsletter #30: 🚀 Universal-1 Model Launch
This week, we’ve launched Universal-1, our most powerful and accurate Speech-to-Text model to date, trained on 12.5M hours of multilingual audio data
Hey 👋, this weekly update contains the latest info on our new product features, tutorials, and our community.
🚀Universal-1 Model Launch
This week, we’ve launched Universal-1, our most powerful and accurate Speech-to-Text model to date, trained on 12.5M hours of multilingual audio data.
Key Highlights of Universal-1:
- 71% better speaker count estimation and 14% better word timestamp estimation compared to our prior models
- Up to 30% fewer hallucinations compared to Whisper Large-v3, ensuring cleaner, more reliable transcriptions.
- Over 22% more accurate compared to speech-to-text APIs from Azure, AWS, and Google.
- Ability to code switch, transcribing multiple languages within a single audio file.
- And, it processes an hour of audio in just 38 seconds. ⚡️
Check out our docs to start building with Universal-1.
Fresh From Our Blog
Transcribe an audio file with Universal-1 using Go: Learn how to transcribe an audio file in your Go applications with industry-leading accuracy using Universal-1. Read more>>
Automatically redact PII from audio and video with Python: Learn how to automatically redact Personal Identifiable Information (PII) from audio and video files in 5 minutes using Python and AssemblyAI. Read more>>
How to do Speech-To-Text with Go: Integrate speech recognition into your Go application in only a few lines of code. Read more>>
Our Trending YouTube Tutorials
This new model is transforming Speech AI: Accurate, Fast, Cost-Effective: AssemblyAI just launched Universal-1, our most capable and highly trained speech recognition model.
Coding an AI Voice Bot from Scratch: Real-Time Conversation with Python: Learn how to build a real-time AI voice assistant using Python that transcribes real-time speech, generates AI responses, and provides a human-like conversational experience.
How to Build a RAG Application for Multi-Speaker Audio Data: Learn how to build a RAG application in 10 minutes that can take multiple speakers into account when answering a question.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.