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 • <150 ms end-to-end latency for a 1-second utterance on the target board. • ≥90 % command accuracy per language in clean conditions, with graceful handling of unknown input. • Runs fully offline with no external dependencies beyond RKNN runtime. If any additional libraries are required, keep them lightweight and open-source so the final firmware footprint remains small. --- 來源：Freelancer.com | 出價次數：27 | 剩餘時間：6 日 | 專案類型：固定預算