Worth noting Shenava-Koochik itself is a 114M Persian streaming ASR model (from Reza2kn’s Shenava collection), so ASR shouldn’t be the compute bottleneck here, the LLM stage is where “disition” (decision/intent understanding) is failing.
For Persian-language intent/decision-making specifically, general multilingual models like Qwen2.5 or Gemma tend to do noticeably better in Farsi than most “small” English-first models, that’s usually the actual fix rather than the ASR choice. If you want something Persian-specialized instead, PartAI/Dorna-Llama3-8B-Instruct is fine-tuned specifically for Persian instruction-following and reasoning. If you’re running this on Reachy Mini’s onboard Pi, 8B is probably too heavy for real-time local inference though, you’d want a smaller Qwen2.5 (1.5B/3B) or route the LLM call to HF Inference API/Endpoints instead of running it on-device, keeping ASR local and off only the decision step.