Keyword support for models

[astral torrent]

Does keyword feature work for nova-2 model and nova-2-phonecall models ? Or is it only for the base model and nova ?

[steady siren]

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[bronze cedar]

Hi @astral torrent. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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Based on the knowledge sources provided, I can answer your question about keyword support for different Deepgram models:

The keyword feature is not fully supported for Nova-2 models, including nova-2-phonecall. Keywords are primarily designed to work with Base models and have limited functionality with Nova models.

Here are the key points:

1. Keywords are fully supported on Base models:
   Models & Languages Overview mentions that Base models are "Recommended for large transcription volumes and high accuracy timestamps." The keyword feature is designed to work best with these models.

2. Limited support for Nova models:
   According to the Keywords documentation, keywords can be used with Nova models, but their effectiveness may be limited compared to Base models.

3. Nova-2 models do not fully support keywords:
   In a GitHub discussion, it was stated that "Keywords are beta feature and they don't currently work with Nova." Keywords seems to have no effect when using tier nova

4. Alternatives for Nova-2:
   For Nova-2 models, including nova-2-phonecall, the focus is on providing accurate transcription without relying on keyword boosting. As mentioned in another GitHub discussion, "The goal is to make it just to give you the correct transcription. Having said that... That is easier said than done. With all the advances in speech recognition, the need for Alternatives isn't needed." Nova-2 models fail when specifying alternatives in request

In summary, while the keyword feature is available and works best with Base models, it has limited functionality with Nova models and is not fully supported for Nova-2 models, including nova-2-phonecall. If keyword boosting is crucial for your use case, you may want to consider using a Base model or exploring custom model training options for more advanced vocabulary recognition.

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