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Nvidia debuts Nemotron 3.5 Transcribe, an open-weight model for multilingual audio transcription and translation, and Granary v2, its open-source speech dataset

Nvidia Corp. today released a leaderboard-winning open artificial intelligence model for multilingual audio transcription and translation …

SiliconANGLE Kyt Dotson

Context & Ripple Effects

Nvidia has been assembling Nemotron into an open-model portfolio rather than a single flagship: March's Nemotron 3 Super release was followed by the multimodal Nano Omni model and August's Lightning model and NeMo Switchyard routing library.

The October release of Super VL for enterprise agents extended that portfolio toward workplace automation; adding speech weights and a speech dataset supplies an input layer for applications that need to turn recorded conversations into usable AI context.

First-order effects

  • Developers gain open weights for multilingual transcription and translation alongside Granary v2, giving them both a model and speech-data resource to adapt for audio workloads.
  • Nvidia expands Nemotron from text, multimodal and visual-language models into speech, making the family relevant to applications built around recorded meetings, calls and other audio.

Second-order effects

  • Speech-model providers face a more capable open-weight baseline, shifting differentiation toward adaptation, deployment, speaker handling and workflow integration rather than transcription alone.
  • Nvidia's earlier NeMo Switchyard release gains a broader model catalog to route across, strengthening its pitch to builders assembling multi-model AI systems.

Third-order effects

  • If Nvidia continues releasing open models across input types, the competitive unit shifts from a standalone model to an interoperable stack of weights, data and tooling.
  • Open releases of both speech models and datasets can make audio a standard component of enterprise-agent pipelines, increasing pressure on vendors to own the surrounding runtime and workflow layer.

The trend: Nvidia is broadening Nemotron into an open, multimodal AI stack in which speech, vision, text and model-routing tools are distributed together for enterprise builders.

Discussion

  • @voicearena_ai @voicearena_ai on x
    🏆 NVIDIA Nemotron 3.5 Transcribe debuts at #1 among 14 systems on Voice Arena's Diarization + ASR leaderboard. Across the full benchmark, it achieves 32.43% cpWER. This track tests whether a system can get both the words and the speakers right. We evaluated 139 held-out conversat…
  • @alex_tra_memory Alex on x
    NVIDIA Nemotron 3.5 Transcribe, ported to Apple's Core ML and Core AI on M5 Pro zero day launch. Core ML (video): a 49.5-min, 3-person meeting → speaker-labeled transcript with word timestamps in 6.0s , 500 rtfx . - Speech encoder + CTC head on the GPU - Nemotron-3-Diarization on…
  • @vllm_project @vllm_project on x
    🎉 NVIDIA Nemotron 3.5 Transcribe is here—with day-0 serving through vLLM + NeMo Speech. 40 languages. Recordings up to an hour. Promptable transcripts, speaker labels and audio Q&A. Open weights in BF16, FP8 and NVFP4.
  • @ivanfioravanti Ivan Fioravanti on x
    NVIDIA Nemotron 3.5 Transcribe rocks! I had it in early access and it's ultra versatile, give it a try!
  • @artificialanlys @artificialanlys on x
    NVIDIA's Nemotron 3.5 Transcribe ties for #1 among open weights Speech to Text models with 2.8% WER, using just 4B active parameters Nemotron 3.5 Transcribe is NVIDIA's new open weights Speech to Text model for non-streaming transcription, with 30B total and 4B active parameters,…
  • @miaai_lab Mia on x
    NVIDIA released Nemotron 3.5 Transcribe 🎙️ - 4B active params - Supports 40 languages - Ranks #1 open-weight model on huggingface - Leads on what was said & who said it. Could run perfectly on a DGX Spark.
  • @varien @varien on x
    NVIDIA solved transcription, this is the perfect scene to demonstrate that. Will report back with my own findings!
  • r/LocalLLaMA r on reddit
    nvidia/NVIDIA-Nemotron-3.5-Transcribe- 30B-A4B  · Hugging Face