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

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.

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 . Quantized versions are supported, so you can run Transcribe locally on your RTX Spark, DGX Spark, or Jetson AGX Thor—or scale up on Blackwell data-center GPUs.

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.

Guide transcription and analysis with natural language

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

- “Transcribe this recording and label each speaker.”

- “Translate this audio into English.”

- “What decisions were made in this meeting?”

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.

For unfamiliar terms, try context before training. In a healthcare setting, for example:

Transcribe this medical discussion in the original language. Possible terms: HbA1c, metformin. Use these terms only when supported by the audio. Preserve standard medical spelling and write numbers in conventional written form.

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.

From live classes to lasting lessons. Agora, whose real-time voice and video platform powers live conversations in apps, used word-level timestamps in a classroom demo: Nemotron 3.5 Transcribe turns a recorded class into a transcript shown alongside the replay, so students can see what was said and when. Agora’s blog walks through how they built it.

**Leaderboard wins from transcription to speaker attribution**

Among open-weight models, Transcribe leads on what was said; on who said it, it tops two speaker-attribution benchmarks outright.

Hugging Face Open ASR Leaderboard. The leaderboard measures transcription accuracy as word error rate (WER) on public test sets, in three tracks:

- English: first among open-weight models at 5.33% average WER (Figure 1).

- Multilingual: 4.62% mean WER across seven languages, the lowest among evaluated open-weight models in the model card’s Open ASR comparison.

- Long-form: 10.98% mean WER, also the lowest in that comparison.

Figure 1. Nemotron 3.5 Transcribe ranks first among open-weight models on the Hugging Face Open ASR Leaderboard (English) at 5.33% average WER, with API models excluded. Lower WER means fewer transcription errors.

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.