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NVIDIA Makes Nemotron 3.5 Transcribe Available as an Open-Weight Model for 40 Languages Under OpenMDW-1.1, With Granary v2 Training Data

NVIDIA says its open-weight Nemotron 3.5 Transcribe handles transcription, speaker labels, translation and audio Q&A in one checkpoint, with benchmark results that are NVIDIA's own and a model built for recorded audio.

AI & Machine Learning Models
Nvidia Nemotron speech recognition open-weight models Granary
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Overview

NVIDIA has made Nemotron 3.5 Transcribe available on Hugging Face and ModelScope. In a Hugging Face community post dated October 28, 2026 and written by NVIDIA authors including Maryam Motamedi and Piotr Żelasko, the company calls it “an open-weight, promptable model” that brings transcription, speaker attribution (diarization), translation and audio question answering into one checkpoint. According to the same post, it transcribes 40 languages, translates 31 of them to or from English and attributes speakers in 13. The accuracy figures below are NVIDIA’s own, as published in that post and the model card, both read on November 3, 2026.

What We Know

Model and license. The model card lists the license as OpenMDW-1.1 and states that the model “is ready for commercial or non-commercial use.” The Herald previously reported on the Linux Foundation’s request that the Open Source Initiative approve OpenMDW-1.1, under the headline “Linux Foundation Asks OSI to Approve OpenMDW-1.1 AI Model License as Reviewers Split Over Its Copyright Termination Clause.” The model card gives the total size as 34B parameters with 4.3B active, including the audio encoder, and a 65,536-token context. The blog post describes a hybrid Mamba-Transformer mixture-of-experts language model in which only about 4B of 30B parameters are active per token. The card names the Nemotron 3.5 Lightning 30B-A3B model as the language backbone; the Herald covered that model’s release as “Nvidia Releases Nemotron 3.5 Lightning, a 30-Billion-Parameter Open Model Built for High-Volume AI Agent Execution.”

Prompting. Per the blog post, one checkpoint can produce different outputs depending on the instruction, such as “Transcribe this recording and label each speaker.” The post says it handles recordings up to an hour long and labels up to eight speakers. It adds that audio question answering is trained in English.

Benchmarks (NVIDIA’s reporting). The post says the model is first among open-weight models on the Hugging Face Open ASR Leaderboard for English at 5.33% average word error rate, with API models excluded. It says the model ties for first among open-weight models at 2.8% AA-WER on the Artificial Analysis speech-to-text leaderboard. For speaker attribution, it reports an 18.26% diarization error rate on David AI’s 21-language leaderboard, against 18.38% for NVIDIA’s Nemotron 3 Diarization model, and a 32.4% concatenated minimum-permutation word error rate on VoiceArena’s English benchmark. The model card’s own table reports a 4.71 mean WER on nine public Open ASR Leaderboard sets, with Qwen3-ASR-1.7B at 4.94 as the next-best open-weight model, in results it says were captured on October 12, 2026. The two sources give different English figures, and the text read does not say why.

Granary v2 data. NVIDIA also released Granary v2, a speech dataset it says was prepared with a reusable NeMo Curator pipeline. According to the post, the public manifest covers 463,062 hours across 48 languages and 16 source corpora, up from 25 languages in Granary v1. The post adds that these figures “describe the dataset release, not Transcribe’s full training mixture or its sampling weights.” Each recording gets three transcript forms, including verbatim and inverse-text-normalized variants, and the post says users must download source audio separately under each corpus’s original license.

Fine-tuning case study. In a medical-speech case study on Eka Care’s benchmark, the post says that adding a better prompt cut word error rate on the full test set from 30.8% to 23.8%, and that fine-tuning cut held-out WER from 22.44% to 12.17%, a 45.8% relative reduction. It says the adaptation took about 14 GPU-hours on one NVIDIA GB300. It also reports that LibriSpeech test-clean WER rose from 1.54% to 1.69%.

Deployment. The post lists vLLM and SGLang-Omni for self-hosting and hosted endpoints from Baseten, DeepInfra and Simplismart. The model card lists a deployment target of one H100 80 GB GPU.

What We Don’t Know

  • Offline only. The post says the model is “built for recorded audio, not live streaming,” and the model card states: “This is an offline model.”
  • Throughput caveats. The post reports processing 100 hours of audio in under three minutes on one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU using the “forthcoming NVFP4 quantized version.” The model card says its serving optimizations “are not yet part of the open-source release” and that the vLLM integration shipped on release day is “expected to be roughly two times slower.”
  • Error modes. The model card says errors include “substitutions, deletions, repetitions, hallucinated speech, incorrect language choice, and speaker attribution errors,” and recommends targeted testing for long recordings, overlapping speakers, noise, names and code-switching.
  • Independent verification. Both sources used here come from NVIDIA, and the leaderboard standings are reported through NVIDIA’s own write-up. This article did not independently reproduce any of the figures.