AssemblyAI
The best way to build Voice AI apps with one robust API
4.8•30 reviews•6.9K followers
The best way to build Voice AI apps with one robust API
4.8•30 reviews•6.9K followers
AssemblyAI builds advanced speech language models that power next-generation voice AI applications. Its industry-leading speech-to-text delivers highly accurate transcription along with speaker detection, summarization, PII redaction, LLM gateway, and a Voice Agent API.
With async and real-time streaming support, developers can easily integrate AssemblyAI into AI notetakers, voice agents, AI medical scribes, call analytics tools, and more.
This is the 10th launch from AssemblyAI. View more
Universal-3.5 Pro
Launching today
Universal-3.5 Pro is AssemblyAI's most accurate speech-to-text model, now available at our Realtime & Async endpoints. It transcribes every conversation exactly as it's heard—code-switching across 18 languages, our most accurate speaker diarization yet, and contextual prompting to steer results.
Free
Launch Team
AssemblyAI
Hey Product Hunt 👋 Happy to be back with another model from the team at AssemblyAI, and today we're launching Universal-3.5 Pro Async.
If you've ever built on top of transcription, you know the transcript is the first mile: everything downstream—summaries, agents, analytics, search—is only as good as the words you start with. So we focused this release on getting that first mile right, especially for the messy, multilingual, real-world audio that most models still stumble on.
Three things we're most excited about:
🌍 Native code-switching across 18 languages. When a speaker moves from English to Spanish and back mid-sentence, Universal-3.5 Pro transcribes the mix in the language it was actually spoken—no separate models, no configuration, no mangled boundaries. It's native to the model across English, Spanish, German, French, Portuguese, Italian, Turkish, Dutch, Swedish, Norwegian, Danish, Finnish, Hindi, Vietnamese, Arabic, Hebrew, Japanese, and Mandarin.
🗣️ Our most accurate speaker diarization yet. Cleaner "who said what," including on short turns, overlapping speech, and noisy environments where diarization usually falls apart. [Drop in the DER / cpWER improvement figure from the post.]
✍️ Contextual prompting. Give the model a plain-language prompt about your audio—the domain, the scenario, the names and jargon that matter—and it biases toward getting those right. No brittle vocabulary lists to maintain.
— Devon & the AssemblyAI team
This is really interesting from a filmmaker/interviewer perspective. I immediately think of long interviews, oral history projects and documentary archives where the real value is not just transcription, but being able to find the exact moment someone said something important.
Curious how well AssemblyAI handles long-form interviews with multiple speakers, accents and imperfect field audio. Do you see makers using this for media archives and documentary workflows too, or is your main focus now voice agents and product teams?