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Nemotron Transcribe — Intelligent Transcription Engine
Audio in, instruction-guided transcript out: describe the output style you need and the model follows it
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nemotron transcribe

Nemotron transcribe is an open-weights speech model: instruction-set styles, 40 languages, one-hour audio, speaker-attributed timestamps.

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Why Use Nemotron Transcribe

No single transcript fits every reader. Nemotron Transcribe lets you name the convention you want in plain language and follows it.

  • Instruction-Steered Transcription Styles
    Verbatim, written-form, contextual and speaker-attributed styles, chosen per request by instruction.
  • Speech-Augmented Language Model Architecture
    A speech encoder, a frozen diarization model and a 30B-A3B hybrid MoE LLM at about 4.3B active parameters.
  • Open Weights and Fine-Tuning Toolkit
    OpenMDW-1.1 weights in BF16, FP8, NVFP4, GGUF and DFlash2 form, plus the NeMo Speech toolkit for Nemotron Transcribe.

How to Run Nemotron Transcribe

Serve a checkpoint, write the instruction, then decode and align — three steps to an attributed transcript.

Nemotron Transcribe Features

Leaderboard-leading accuracy, speaker attribution for overlapping speech and fast NIM inference — what Nemotron Transcribe brings.

Open ASR Leaderboard Accuracy

Mean WER of 4.71% over the 9 English Open ASR test sets and 4.62% on the multilingual board.

Speaker-Attributed Transcription for Overlapping Speech

From one-minute segments to full meetings, it is the first SALM to beat cascaded systems on attributed ASR.

Contextual Transcription with Entity Lists

Expected entities cut named-entity errors six-fold on ContextASR-Bench, driven by the parallel expert encoder.

Speaker-Attributed Word Timestamps

A small CTC adapter maps the finished transcript back onto the audio, so long overlapping calls still get per-speaker timings.

Efficient Inference and Throughput

The model's MTP layer or a DFlash2 draft speeds decoding; the NVFP4 NIM does 2,238 audio seconds per second on one RTX PRO 6000 Blackwell.

Single-GPU Adaptation to New Tasks

A supervision-balanced mixture lets one GPU pick up new languages and domains in about 0.5–2.3 GPU-hours per task.

FAQ

Nemotron Transcribe — FAQ

Common questions about Nemotron Transcribe: benchmarks, languages, timestamps, licensing and checkpoints.

1

What is Nemotron 3.5 Transcribe?

A speech-augmented language model joining a speech encoder, a frozen diarization model and a 30B-A3B hybrid MoE LLM.

2

Which transcription styles can I request?

Verbatim, written-form, contextual, speaker-attributed and rich transcription, plus translation and language ID, each set by an instruction.

3

How accurate is it on public benchmarks?

4.71% mean WER on the 9 English leaderboard sets, 4.62% multilingual, and six-fold fewer named-entity errors on ContextASR-Bench.

4

How long can recordings be, and how many languages are supported?

One Nemotron Transcribe checkpoint handles recordings up to an hour across 40 languages, code-switched speech included.

5

Can it tell me who spoke and when?

Yes — overlapping multi-speaker audio is attributed per speaker, and per-speaker word timestamps come back on request.

6

What license and checkpoints are released, and can I fine-tune?

OpenMDW-1.1 weights ship as BF16, FP8, NVFP4, GGUF and DFlash2 files; NeMo Speech fine-tunes them in about 0.5–2.3 GPU-hours per task.

Start Transcribing with Nemotron Transcribe

Pick a checkpoint, serve it with the Nemotron Transcribe NIM, and send your first instruction-driven request — styles, translation, language ID and speaker timestamps in 40 languages.