Universal-2: A next-gen speech-to-text model pushing beyond traditional WER (word error rate) metrics. Built on Universal-1's industry-leading performance in just 6 months.
Key results:
24% better at recognizing proper nouns
21% improvement in alphanumeric accuracy
15% enhanced text formatting
73% of users prefer Universal-2 compared to Universal-1
Overall more accurate and robust model especially on real-world speech complexity
Sets new standards across human and technical benchmarks
Architecture:
Smart architecture choices prioritized over simply scaling model size
Universal-2 uses a 660M parameter Conformer RNN-T model
Built an innovative all-neural formatting pipeline
Solved critical challenges like repeated token handling in RNN-T
Announcement Landing Page: https://www.assemblyai.com/universal-2
Try it yourself: https://www.assemblyai.com/playground
Google colab: https://colab.research.google.com/dri...
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