NVIDIA Releases Open Nemotron 3.5 Transcribe Speech Model and Granary v2 Data
NVIDIA released Nemotron 3.5 Transcribe, an open-weight promptable speech model that combines transcription, speaker attribution, translation, and audio question answering in one checkpoint. It transcribes 40 languages, translates 31 to or from English, attributes speakers in 13, handles recordings up to an hour, and labels up to eight speakers. The post also describes Granary v2 multilingual data, a curation pipeline, and NeMo Speech fine-tuning tools for domain adaptation.
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NVIDIA released Nemotron 3.5 Transcribe, an open-weight promptable speech model that combines transcription, speaker attribution, translation, and audio question answering in one checkpoint. It transcribes 40 languages, translates 31 to or from English, attributes speakers in 13, handles recordings up to an hour, and labels up to eight speakers. The post also describes Granary v2 multilingual data, a curation pipeline, and NeMo Speech fine-tuning tools for domain adaptation.
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Benchmark results are reported by NVIDIA and third-party leaderboards as described in the post; source claims not independently checked. Quantized versions and local deployment are noted as coming soon, and the model is built for recorded rather than live-streaming audio.
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Key findings from the stored brief
- Transcribe supports 40 transcription languages, translation for 31 languages to or from English, and speaker attribution in 13 languages.
- On the Hugging Face Open ASR Leaderboard, it ranks first among open-weight models for English at 5.33% average WER.
- On David AI's diarization track across 21 languages, it posts the lowest diarization error rate of 14 systems tested (18.26%).
- In a medical case study, fine-tuning cut word error rate on held-out speakers by 45.8% relative to the best prompt.
Benchmark results are reported by NVIDIA and third-party leaderboards as described in the post; source claims not independently checked. Quantized versions and local deployment are noted as coming soon, and the model is built for recorded rather than live-streaming audio.
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Evidence & sources
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Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
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NVIDIA released Nemotron 3.5 Transcribe, an open-weight promptable ~30B MoE (~4B active) that transcribes 40 languages, translates 31 to or from English and labels speakers in 13, with the Granary v2 data pipeline and fine-tuning recipes. NVIDIA reports it is first among open-weight models on the HF Open ASR English track; Artificial Analysis independently ranks it tied #1.
Benchmark results are reported by NVIDIA and third-party leaderboards as described in the post; source claims not independently checked. Quantized versions and local deployment are noted as coming soon, and the model is built for recorded rather than live-streaming audio.
- Bot tagged: supports Artificial Analysis says Nemotron 3.5 Transcribe ties for #1 among open-weights speech-to-text models at 2.8% WER on its benchmark. Inspect post ↗
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Why this may matter
An open, promptable model that handles transcription, speaker labels, and translation together lowers the barrier for developers building meeting, classroom, and medical transcription tools, and offers fine-tuning paths for domain vocabulary and accents.
Limits and open questions
Benchmark results are reported by NVIDIA and third-party leaderboards as described in the post; source claims not independently checked. Quantized versions and local deployment are noted as coming soon, and the model is built for recorded rather than live-streaming audio.
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