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nemotron transcribe
Use Nemotron Transcribe to turn hour-long audio in 40 languages into styled, speaker-labelled text with word-level timestamps — open weights, precise control.
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Inside Nemotron Transcribe: Instruction-Led Speech Recognition
Every audience expects a different reading experience. With Nemotron Transcribe, you describe the formatting convention in everyday language and the model applies it consistently.
- Style Control Through Plain-Language PromptsChoose verbatim, written-form, contextual or speaker-attributed output per request — no retraining required.
- Speech-Aware LLM BackboneCombines a speech encoder, a frozen diarization module and a 30B-A3B hybrid MoE language model with roughly 4.3B active parameters.
- Open Weights Plus Fine-Tuning SupportDownload OpenMDW-1.1 checkpoints in BF16, FP8, NVFP4, GGUF or DFlash2 format, and adapt them with the NeMo Speech toolkit.
Deploying Nemotron Transcribe in Three Steps
Load a checkpoint, craft the style instruction, then decode and align the output to produce a speaker-aware transcript.
Capabilities That Set Nemotron Transcribe Apart
Top-tier benchmark scores, reliable speaker labels for overlapping voices and high-throughput NIM serving — the core strengths of Nemotron Transcribe.
Benchmark-Topping Accuracy
Achieves a 4.71% mean WER across nine English Open ASR test sets and 4.62% on the multilingual leaderboard.
Speaker Labels for Overlapping Audio
Handles everything from one-minute clips to full meetings, and is the first SALM to outperform cascaded systems on attributed ASR.
Context-Aware Entity Handling
Supplying expected entities reduces named-entity errors by six times on ContextASR-Bench, powered by the parallel expert encoder.
Per-Speaker Word Timestamps
A compact CTC adapter maps the completed transcript back to the audio, preserving per-speaker timing even in long overlapping calls.
Fast Inference and High Throughput
The model's MTP layer or a DFlash2 draft accelerates decoding; the NVFP4 NIM processes 2,238 audio seconds per second on one RTX PRO 6000 Blackwell.
Single-GPU Task Adaptation
A supervision-balanced mixture lets one GPU learn new languages and domains in roughly 0.5–2.3 GPU-hours per task.
Nemotron Transcribe: Common Questions Answered
Find answers on benchmark results, language coverage, word timestamps, licensing and available checkpoints for Nemotron Transcribe.
What exactly is Nemotron Transcribe?
It is a speech-augmented language model that combines a speech encoder, a frozen diarization component and a 30B-A3B hybrid MoE LLM.
Which output styles can I request?
You can ask for verbatim, written-form, contextual, speaker-attributed or rich transcription, plus translation and language identification, all controlled by an instruction.
How does it perform on public benchmarks?
It reaches 4.71% mean WER on nine English leaderboard sets, 4.62% multilingual, and cuts named-entity errors six-fold on ContextASR-Bench.
What recording length and language coverage are supported?
A single checkpoint processes recordings up to one hour across 40 languages, including code-switched speech.
Does it identify speakers and timing?
Yes. Overlapping multi-speaker audio is labelled per speaker, and per-speaker word timestamps are returned when requested.
What license and checkpoints are available, and can I fine-tune?
OpenMDW-1.1 weights are released as BF16, FP8, NVFP4, GGUF and DFlash2 files; NeMo Speech fine-tunes them in about 0.5–2.3 GPU-hours per task.
Put Nemotron Transcribe to Work
Choose a checkpoint, deploy it through the Nemotron Transcribe NIM, and send your first instruction-based request — style control, translation, language detection and speaker timestamps across 40 languages.