Many languages are predominantly spoken rather than written, and to bring the benefits of LLMs to speakers of these languages, it is essential that models cater to the voice modality. The typical approach is to cascade ASR, LLM and TTS models together, though this results in systems with high latency, making them unsuitable for natural, real-time interaction. We describe results on taking the encoder part of a Whisper-based model trained to recognise ten languages common in Uganda, and using the Ultravox architecture to project its output directly to the input embedding space of a text model based on Qwen 3 32B, also trained to have comprehension of those languages. The result is a speech LLM with high accuracy and very low latency. For most spoken prompts, we can begin streaming a text response within as low as 50 ms, and a speech audio response within around one second, making real-time spoken interaction with an LLM possible for the first time in these languages. The model is available open source onHugging Face.

We present the SALT-31 benchmark dataset for evaluation of machine translation models covering 31 Ugandan languages. Unlike sentence-level evaluation sets, SALT-31 is constructed from short, scenario-driven mini-dialogues designed to preserve discourse context, pragmatics, and culturally grounded communication patterns common in everyday Ugandan settings. The dataset contains 100 English sentences organized into 20 typical communication scenarios, each represented as a five-sentence mini-sequence. It can therefore be used to evaluate both sentence-level and paragraph level machine translation, and includes nearly every language spoken in a country with high linguistic diversity. It is available at https://huggingface.co/datasets/Sunbird/salt-31