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NVIDIA's Open Nemotron 3.5 Transcribe Puts Transcripts, Speaker Labels and Translation in One Model, for Recorded Audio Only

NVIDIA released Nemotron 3.5 Transcribe on October 28, an open-weight 30B-parameter model with about 4B active that transcribes 40 languages, translates 31 and attributes speakers in 13, for recordings up to an hour. It also released the Granary v2 speech dataset. NVIDIA's leaderboard claims, including first among open models on Open ASR English, are its own; the model does not support streaming.

Times of AI Desk 3 min read Santa Clara, California View as Markdown
A bronze bell in a wooden frame sends sound ripples across the scene that turn into fine threads winding onto a spinning wheel.

NVIDIA's new speech model is aimed at a job that usually takes several systems: turning a long recording into a transcript that says who spoke, and translating it. Nemotron 3.5 Transcribe, released with open weights on October 28, handles transcription in 40 languages, translation for 31 and speaker attribution for 13 in one model, according to NVIDIA. What it does not do is live audio: NVIDIA says it is built for recorded files, not streaming.

What NVIDIA released#

The model has 30 billion total parameters with about 4 billion active per token, using a hybrid Mamba-Transformer mixture-of-experts design, NVIDIA says. It takes recordings of up to an hour with up to eight speakers. NVIDIA also released Granary v2, a speech dataset it describes as 463,062 hours across 48 languages, and said quantized versions of the model are coming.

TechCrunch framed the release as part of a wider push into voice AI. Mistral released its own Voxtral 3 speech models last week, and NVIDIA's previous Nemotron 3.5 release was the Super VL multimodal model.

The leaderboard claims#

NVIDIA reports a 5.33% average word error rate on the Hugging Face Open ASR leaderboard for English, first among open-weight models, and a 2.8% score on Artificial Analysis's AA-WER, tied for first among open-weight models. It also reports an 18.26% diarization error rate on a David AI benchmark and 32.4% on VoiceArena. NVIDIA says a medical fine-tune cut word error rate by 45.8% relative to the base model on its tests.

Limits#

  • All benchmark and leaderboard figures above are as reported by NVIDIA. We did not check the Open ASR or AA-WER boards for this report.
  • No streaming or real-time support; NVIDIA says the model is for recorded audio.
  • We did not review the licence terms for commercial use.

Sources#

Prior Coverage

Earlier Times of AI reporting on this thread.

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