NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

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.

Transcribe is first among open-weight models on the Hugging Face Open ASR Leaderboard for English and sits on Artificial Analysis ’s accuracy–active-parameter frontier. For speaker attribution, it posts the lowest error rate on David AI’s 21-language diarization leaderboard and ranks first in VoiceArena’s 14-system comparison . It runs on a single NVIDIA GPU, from DGX Spark to Blackwell data-center systems.

Artificial Analysis Speech-to-Text Leaderboard. Artificial Analysis independently ranks speech-to-text models by AA-WER, its word error rate index. Nemotron 3.5 Transcribe ties for first among open-weight models at 2.8% AA-WER, with about 4B active parameters versus 24B for the model it ties. On AA-AgentTalk, the held-out voice-agent speech set that makes up 50% of the AA-WER index, Transcribe scores 1.8% WER, versus 2.3% for the next-best open-weight model. Transcribe pairs its speech encoder with a hybrid Mamba–Transformer mixture-of-experts language model, so only about 4B of its 30B parameters are active per token. That puts it on Artificial Analysis ’s open-weight Pareto frontier of accuracy versus active parameters. On AA’s all-model chart, it ranks ahead of several widely used proprietary speech APIs.

Figure 2. Leading AA accuracy at about one-sixth the active parameters: Nemotron 3.5 Transcribe achieves 2.80% AA-WER with approximately 4B active parameters.

A promptable speech model requires training data with unambiguous formatting targets. Granary v2 is part of the Nemotron speech collection on Hugging Face and ModelScope . Built with NVIDIA NeMo Curator , its reusable pipeline prepared multilingual speech used in Nemotron 3.5 Transcribe’s training, producing aligned transcript variants, task labels, and inspectable quality metadata. The public manifest covers 463,062 hours across 48 languages and 16 source corpora, up from 25 languages in Granary v1 . These figures describe the dataset release, not Transcribe’s full training mixture or its sampling weights.

Granary v2’s multilingual data (available on Hugging Face and ModelScope ) and reusable curation pipeline, together with NeMo Speech fine-tuning tools , extend prompt-level control to training: developers can define the written form they need and adapt the model to the speech their applications encounter. In a medical case study , fine-tuning cut word error rate (WER) on held-out speakers by 45.8% relative to the best prompt.

Granary v2 generates three transcript forms for each recording (Table 1), the same verbatim and clean styles developers can request from Transcribe with a prompt. Beyond transcription, the pipeline produces short audio captions, code-switching restoration of loanwords, speech-grounded Q&A pairs, contextual ASR biasing terms, and translations with automatic quality scores.

Developers can run the NeMo Curator pipeline with Ray and NVIDIA Dynamo backends on their own recordings or authorized public corpora, or build on the Granary v2 public manifests . The manifests include the three transcript forms as tn_raw, itn_raw, and itn_clean, the labels described above, entity hints, derived instruction pairs, provenance, and a per-record quality flag. Download source audio separately under each corpus’s original license, and follow the release’s published terms.

The model card has recommended presets, including reasoning for audio questions, plus the full prompt guide and a task-by-language matrix: language support differs by task, and audio question answering is trained in English. Word-level timestamps are also supported. As with any speech model, spot-check names, speaker changes, and answers against the recording.

The same checkpoint can turn one meeting recording into different outputs, depending on the instruction:

Experience the model in action in the video below, where it tackles a classic scene from the 1940 screwball comedy His Girl Friday—a film famous for its rapid-fire, heavily overlapping dialogue.

Nemotron 3.5 Transcribe captions a scene from His Girl Friday (1940) and labels speakers, including overlapping dialogue.