Nemotron 3.5 Transcribe: text, translation and speakers

Interview preparation often needs more than recognized words: participant labels, accurate terminology and sometimes translation. NVIDIA’s October 28 article presents Nemotron 3.5 Transcribe as one model for several speech tasks, alongside Granary v2 data and adaptation tools.

Reported capabilities

The authors report transcription in 40 languages, translation between English and 31 languages, and speaker attribution in 13 languages, with recordings up to one hour and up to eight speakers. These are different support sets. Transcription support for a language does not establish support in every other mode.

Natural-language instructions can request a transcript with speakers, translation or answers about the audio. Evaluate these separately: accurate words, correct speaker labels and a faithful answer about a meeting are different outcomes.

Start with your own reference sample

Choose a short representative excerpt before attempting a full interview. Prepare a manual reference for important phrases, names, numbers and speaker changes. First request a transcript in the original language. Add speaker labeling or translation in a separate task so the source of errors remains visible.

An editorial example is a conversation containing unusual tool names. A vocabulary list can provide context, but each resulting term must still be checked against the recording. A spelling hint should not become an instruction to insert words that were never spoken. This is a validation procedure, not a model run we performed.

What to evaluate

• Compare missing and substituted words with the reference transcript.
• Review names, numbers, units and specialized vocabulary separately.
• Inspect speaker changes, especially during interruptions.
• For translation, check meaning, negation and attribution.
• Match answers about the recording to identifiable audio passages.

Fluent output is not proof of an accurate transcript. For publication, preserving meaning and speaker attribution matters more than smooth prose containing unnoticed substitutions.

Where Granary v2 fits

The article describes multilingual data and a reusable preparation pipeline, complemented by NeMo Speech adaptation tools. This is a more involved stage than adding context to a recognition request.

Our recommendation is to measure recurring errors on a small labeled sample first. A few troublesome terms may warrant testing context instructions. Persistent domain or accent errors create a more specific case for adaptation. The existence of a dataset does not make fine-tuning immediate or free.

Limits of benchmark and cost claims

Supplier speed and quality measurements depend on particular datasets, hardware and workloads. Do not transfer them directly to a home computer, long recordings or different concurrency. Open weights still require compute: local hardware or resources at the chosen hosting provider.

No paid test was performed for this article. It explains reported capabilities and a way to validate them, not confirmed VIZURAI availability. A useful first editorial result is a small reference set with understood errors and processing cost.