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.
The Universal-1 Speech-to-Text model was created to focus on the nuances of spoken language across accents, tone, dialect, faithfulness, and more. We hope the new capabilities of Universal-1 will help power the next generation of AI products and features built with voice data. Give it a try and let us know what you're building!
Great launch! How does Universal-1 handle multilingual support and accuracy across different languages?
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!
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!
Congratulations on the launch! How did you manage to ensure high accuracy for languages with fewer training data?
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"?
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?
Congratulations on the launch 🎊
I think this can be used for converting podcasts into blogs.🤔
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?
Cool!👍 The free version can transcribe up to 100 hours of audio, It's sufficient for the initial use of the project.
Oh my god, Universal-1 is hard-tech product!
Happy to find this product in here, may you good luck on this launch🎉
12.5M hours of data is not easy to collect, and you guys done it!
About Universal-1 on Product Hunt
“Multilingual speech AI model trained on 12.5M hours of data”
Universal-1 launched on Product Hunt on May 21st, 2024 and earned 421 upvotes and 20 comments, placing #13 on the daily leaderboard. 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.
Universal-1 was featured in Developer Tools (511k followers), Artificial Intelligence (466.2k followers) and Audio (2k followers) on Product Hunt. Together, these topics include over 155.6k products, making this a competitive space to launch in.
Who hunted Universal-1?
Universal-1 was hunted by Meredith Rauch. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Universal-1 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Meredith Rauch