# Nemotron3.5 Transcribe and Granaryv2: prompted vocabulary and faithful transcription

NemotronTranscribe's prompted vocabulary can help a recognizer consider rare terms, but it should not make supplied words count as evidence that they were actually spoken.

Author: Retrivium editorial team
Language: en
Analysis published: 2026-11-03
Event date: 2026-10-28
Original announcement: https://huggingface.co/blog/nvidia/nemotron-transcribe-granary
Canonical URL: https://retrivium.ai/retrospectives/retro-nemotron35-transcribe-prompted-asr/

NemotronTranscribe's prompted vocabulary can help a recognizer consider rare terms, but it should not make supplied words count as evidence that they were actually spoken.

## A hint and an observation differ

A vocabulary hint changes the information available to recognition. The audio remains the source for a faithful transcript. If an application treats the hint as an answer list rather than contextual guidance, it can reward inserting plausible terms where the recording does not support them.

## The October prompted-speech event

The [Transcribe announcement](https://huggingface.co/blog/nvidia/nemotron-transcribe-granary) introduces prompted speech processing alongside Granary workflows. The earlier [Whisper paper](https://arxiv.org/abs/2212.04356) and [Granite speech paper](https://arxiv.org/abs/2505.08699) supply dated context. They do not establish that any particular vocabulary list improves every recording or that prompted outputs are automatically source-faithful.

The useful question is not merely whether the desired rare word appears more often. It is whether the interface recovers supported occurrences while preserving the distinction between the supplied context and what the speaker actually said.

## A technical-name example

Imagine providing a product glossary before transcribing a maintenance call. Some recordings contain a rare product name, while others contain an acoustically similar ordinary word. A fixture containing only positive glossary occurrences can make an over-eager recognizer look useful while hiding inappropriate insertions.

| Case | Acceptance emphasis |
| --- | --- |
| Glossary term clearly spoken | Recover its source-supported occurrence. |
| Similar sound with another meaning | Preserve the actual spoken content. |
| Listed term absent from the recording | Do not fill the transcript from the hint alone. |

## Keep hints in the provenance record

The same audio with a changed vocabulary list is a different effective recognition input. Preserve the list and any associated instructions with the output. Otherwise a later reviewer cannot tell whether changed wording came from the recording, the model revision or a different prompt supplied by the application.

Keep raw transcription separate from later terminology normalization. A business system may intentionally standardize names, but that derivative should not overwrite the evidence of what the recognizer heard. The distinction allows a useful normalized field without making the literal transcript falsely claim greater certainty.

## The retrospective lesson

The release expands control over speech recognition through contextual input. Its value is strongest when evaluation includes both recovery and resistance to unsupported completion, rather than counting every appearance of a desired term as success.

The conclusion is to test positive and negative vocabulary cases together. Promptability can support domain work, while faithful transcription still requires an acceptance rule grounded in the audio. Context may guide interpretation; it cannot replace the source.

Further analysis: [Reproducible inference stacks: preserving a GPU pilot’s result](/journal/software-stack-reproducibility/); [GPU hosting for multimodal models: count more than text](/journal/multimodal-gpu-hosting/).