By hackernoon
Publication Date: 2026-10-30 00:00:00
Overview
NVIDIA Nemotron 3.5 Transcribe 30B A4B is an open-weight, offline speech-and-audio model that turns recordings and text instructions into text. It supports transcription, translation, speaker-attributed and contextual transcription, timestamps, language identification, and audio question answering across 40 languages. The model is maintained by nvidia.
Best use cases
- Transcribe recordings verbatim, or produce readable text by omitting fillers and accidental repetitions.
- Improve recognition of names and specialist vocabulary by supplying context terms.
- Create speaker-attributed transcripts and request word timestamps for meeting review.
- Translate speech into a specified target language.
- Identify the language spoken or ask questions about audio.
- Process long recordings offline; the model supports recordings up to one hour.
Limitations
This is an offline model, not a real-time streaming ASR service; internal audio chunking does not change that. The stated one-H100 80 GB deployment requirement is substantial. Published benchmark figures are reported for selected datasets and tasks, so they do not establish performance on every language, domain, or recording condition.
How it compares
The supplied results report mean WER of 4.71 for English ASR, 10.98 for long-form ASR, and 4.62 for multilingual ASR across the listed benchmark sets; they identify Qwen3-ASR-1.7B and Parakeet-TDT-0.6B-v3 as next-best open-weight models on those respective evaluations. These are benchmark-specific…
