ARM-software/ML-KWS-for-MCU

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1249 stars · 428 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow models and training scripts for keyword spotting (wake-word detection) on Arm Cortex-M microcontrollers, accompanying the 'Hello Edge' paper. It includes pretrained DNN/CNN/LSTM/GRU/CRNN/DS-CNN models, quantization guidance, and example deployment code for Cortex-M boards.

Use cases

• train a keyword spotting model for microcontrollers
• deploy wake-word detection on Cortex-M
• quantize a neural network for embedded audio inference
• compare DNN CNN LSTM architectures for keyword spotting
• run speech command recognition on an MCU

When to choose

• you need to build always-on voice wake-word detection on resource-constrained Arm Cortex-M hardware
• you want pretrained, quantized keyword spotting models with memory/accuracy tradeoffs from a research paper

When to avoid

• you need modern maintained tooling - the repo is based on old TensorFlow 1.x and last released in 2019
• you need full speech recognition or large-vocabulary models rather than small keyword sets
• you target non-Arm or non-embedded platforms with modern frameworks like TensorFlow Lite Micro

Facets

library · maturity maintenance

machine-learning deep-learning audio-processing speech-recognition llm-training machine-learning deep-learning embedded-systems speech-processing embedded python cpp c cross-platform keyword-spotting cortex-m tensorflow cmsis-nn quantization tinyml wake-word audio

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ARM-software/ML-KWS-for-MCU")