Nvidia on Wednesday launched Nemotron 3.5 Transcribe, its latest multilingual transcription model. The new open-weight model, the company says, ranks first on Hugging Face’s Open ASR open model leaderboard for English, with an average word error rate of 5.33%, and also sits at the top of two speaker-attribution benchmarks.

But what’s maybe even more interesting is that this is a promptable transcription model. Being promptable here means that the user can give the model specific instructions before it listens. Typically, that means setting the context for the transcription by telling the model that a given audio file is a medical discussion in English, for example, and giving it a list of medical terms.

Users can also tell the model to label each speaker or ask it questions about the audio. They can request a verbatim transcript or a cleaned-up version, and tell it how to format numbers, for example.

Nvidia bundles these capabilities, along with translation, into a single checkpoint, which could simplify the work of building applications around them.

Ideally, setting this context reduces the error rate. The trade-off is that these models are much larger than traditional transcription models, which are often under a billion parameters in size. Nemotron 3.5 Transcribe is a 30B mixture-of-experts model with 4 billion active parameters.

This isn’t a completely new concept, though. We’ve recently seen some other promptable speech-to-text models like Mistral’s Voxtral models and AssemblyAI’s Universal 3 Pro.

Nvidia, though, is able to rival Voxtral’s error rate with far fewer active parameters.

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