One open model for multilingual transcription, speaker attribution, translation, and audio Q&A, with top leaderboard results, plus the open Granary v2 data pipeline and fine-tuning recipes to adapt it.
A transcript can capture every spoken word and still leave users or applications wondering who said what, how to translate it, or how to format domain-specific jargon. Multilingual calls, fast-paced meetings, and medical dictation each need different output: a translation, speaker labels, or correctly spelled medical terms.
NVIDIA Nemotron 3.5 Transcribe is an open-weight, promptable model that turns recorded speech into useful text, now available on Hugging Face and ModelScope . It brings transcription, speaker attribution (diarization), translation, and audio question answering into one checkpoint: it transcribes 40 languages, translates 31 of them to or from English, and attributes speakers in 13.
It handles recordings up to an hour long and labels up to eight speakers. Developers can guide output with natural language, choose where their audio is processed, and adapt recognition to domain vocabulary and accents.
Hugging Face Open ASR Leaderboard for English at 5.33% average WER, and ties for first among open-weight models on Artificial Analysis's AA-WER index at 2.8% with about 4B active parameters.
