Offline RKNN Multilingual ASR Rk3576

I need an end-to-end voice command system that runs entirely offline on a Rockchip board using the RKNN NPU. The solution must automatically identify the spoken language and then recognise a fixed set of voice commands without falling back to any cloud service.

Target hardware
The model will run on an embedded system powered by a Rockchip NPU (RK3588 / RK3568 class). Please optimise with RKNN-Toolkit and supply the final *.rknn file together with conversion scripts so I can reproduce the build from the original TensorFlow / PyTorch / ONNX model.

Languages and use-case focus
• Automatic language ID for: English, Mandarin Chinese, Cantonese, Thai, Korean, Japanese and Malay.
• After language detection, the engine should map the utterance to a command ID (no large-vocabulary transcription needed).

Deliverables
1. Trained multilingual model converted and benchmarked on the RKNN NPU.
2. Demo application (C/C++ or Python) that listens on the device microphone, prints the detected language and command, and triggers a GPIO or log action.
3. Clear documentation covering dataset choices, preprocessing, model architecture, quantisation steps, RKNN optimisation settings, and instructions for rebuilding or fine-tuning.

Acceptance criteria
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