Universal-1
Multilingual speech AI model trained on 12.5M hours of data
| About | Details |
|---|---|
| Name: | Universal-1 |
| Submited By: | Paul Kshlerin |
| Release Date | 1 year ago |
| Website | Visit Website |
| Category | Developer Tools Audio |
Try AssemblyAI's most capable and highly trained speech recognition model trained on 12.5M hours of multilingual audio data. Universal-1 achieves best-in-class speech-to-text accuracy, reduces word error rate and hallucinations, and improves timestamps.
Great launch, AssemblyAI! 🚀 Universal-1's focus on multilingual speech recognition and improving accuracy is exactly what the industry needs. This model will make a huge difference in developing global voice applications. Excellent job!
11 months ago
Excited to test this. Does the playground currently allow us to access this model? Or will it soon? I'm assuming part of what makes this work well is the same process you've been using, with word prediction based on context, as opposed to straight word for word phonetic reproduction of what it "hears"?
1 year ago
Congratulations on the launch! It's fantastic to see your product achieving a higher accuracy in voice recognition than many big companies. It's truly impressive and admirable. In the future, if my product development involves voice recognition, I will definitely consider using your API!
1 year ago
Great launch! How does Universal-1 handle multilingual support and accuracy across different languages?
1 year ago
Congratulations on the launch 🎊 I think this can be used for converting podcasts into blogs.🤔
1 year ago
congratulations on the launch of universal-1, dylan, britney, and meredith. your focus on reducing word error rates and enhancing dialect recognition is impressive. could you share how universal-1 handles low-resource languages and if there are plans to expand its linguistic dataset?
1 year ago
Congratulations on the launch! How did you manage to ensure high accuracy for languages with fewer training data?
1 year ago
The Universal-1 Speech-to-Text model sounds incredibly promising! Since I use tools like this often for my work, I was wondering can you share some examples of the unique challenges you faced in achieving high accuracy across different tones and speaking styles?
1 year ago
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